{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "import re\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# 添加项目根目录到Python路径\n",
    "sys.path.append(os.path.abspath('../..'))  # 修改这行，向上追溯三层目录到项目根目录\n",
    "\n",
    "\n",
    "topic = \"what does the current technological development of Text2SQL look like?\"\n",
    "with open(r\"D:\\GoodStudy\\FX15_reference_1\\summary-generation-match\\research_agent\\scripts\\1.md\", \"r\", encoding=\"utf-8\") as file:\n",
    "    content = file.read()\n",
    "def split_by_primary_headers(text):\n",
    "        \"\"\"使用正则表达式匹配一级标题（## 开头的标题）\"\"\"\n",
    "        headers = re.split(r'(?m)^##\\s+', text.strip())\n",
    "        sections = []\n",
    "        for section in headers:\n",
    "            sections.append(section.strip())\n",
    "        return [f\"\\n## {section.strip()}\" for section in headers if section.strip() != \"\"]\n",
    "sections = split_by_primary_headers(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"\\n## 1 Introduction\\n\\nResearch on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query. However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors. For example, a query asking for the capacity of O2 Arena could be ambiguous if the schema has separate columns for standing and seating capacity. Similarly, a query on the number of under-nourished children is ambiguous if there are different columns for 'under-weight children' and 'stunted growth in children'. This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers. This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent. To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity. This benchmark aims to test the performance of Text-to-SQL systems under ambiguity and encourage the development of more robust and accurate models.\\n\\nIn this survey, we explore the current state of Text-to-SQL technology, focusing on the challenges posed by ambiguity and the approaches used to address it. We discuss the limitations of existing benchmarks and evaluation metrics, and propose potential improvements to ensure a more comprehensive and accurate assessment of model capabilities. Benchmarks like PredBench [PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines, 66908e2301d2a3fbfcea14d7, 8] have limitations in terms of training, benchmarking, and evaluation. For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols. Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics. To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance. Additionally, incorporating indicators of attack failures [Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples, 60d140d191e011c16f0cb388, 5] could help in debugging faulty evaluations and lead to fairer assessments. Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments [STEER: Assessing the Economic Rationality of Large Language Models, 65cec1c1939a5f40828f00d7, 11] to evaluate models' ability to exhibit rational behavior in economic tasks. We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems. For example, the AmbiQT benchmark (Benchmarking and Improving Text-to-SQL Generation under Ambiguity, 6535d747939a5f408295c649, 1) addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates. Furthermore, the work on interactive Text-to-SQL generation (Interactive Text-to-SQL Generation Via Editable Step-by-Step Explanations, 6461b9c9d68f896efad43133, 1) proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation. Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL (Exploring Chain-of-Thought Style Prompting for Text-to-SQL, 646d8642d68f896efa0a3040, 1) aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task. Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness. Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.\",\n",
       " '\\n## 2 Background and Related Work',\n",
       " '\\n## 2.1 Early Developments\\n\\nThe early approaches to Text2SQL can be categorized into rule-based systems and grammar-based methods. Rule-based systems, such as the one proposed by Hendrix et al. (1978), relied on handcrafted rules to map natural language questions to SQL queries. For example, PGTune [1] makes configuration recommendations by asking users for basic information about the Postgres database they are using and the details about their hardware environment. Note that the information of the Postgres’ version and the number of CPUs affects the setting of the knobs because a new version will introduce new knobs. For versions below 9.5, max_worker_processes is not available. Similarly, max_parallel_workers_per_gather supports versions higher than 9.5, and max_parallel_workers supports v10 and higher versions. The setting of the knob values also follows the rules. These rules include that the values of max_worker_processes and max_parallel_workers are equal to the number of CPUs and the value of max_parallel_workers_per_gather is half the number of CPUs. These systems were limited in their ability to handle complex queries and required extensive manual effort to create and maintain the rules. Grammar-based methods, like the one developed by Giordani and Moschitti (2012), used generative parsers to translate questions into SQL queries. While these methods offered some flexibility, they still struggled with the inherent complexity and ambiguity of natural language. For instance, the approach necessitates meticulous prompt design to generate exercises, which inevitably entails human intervention, introducing potential bias or variability and may not scale efficiently. Additionally, the quality and correctness of the generated problems are not explicitly addressed, and the current framework relies on a source problem for exercise generation, limiting flexibility and robustness. Furthermore, the handling of ambiguity in natural language is a significant challenge, as models often fail to capture the distribution of possible meanings without deliberate instruction.',\n",
       " '\\n## 2.2 Deep Learning Era\\n\\nThe advent of deep learning brought about a paradigm shift in the field of Text2SQL, enabling the construction of several large text-to-SQL datasets, such as WikiSQL (Zhong et al., 2017) and Spider (Yu et al., 2018), and achieving unprecedented performance in recent years (Rubin and Berant, 2021; Wang et al., 2020a; Scholak et al., 2021; Yu et al., 2020; Hwang et al., 2019). Neural network-based models, particularly sequence-to-sequence models, demonstrated remarkable improvements in translation accuracy and generalization capabilities. Notable examples include Seq2SQL (Zhong et al., 2017), which employed reinforcement learning to generate SQL queries, and RATSQL (Wang et al., 2020a), which introduced a relation-aware self-attention mechanism to better encode the relationships between columns and tables. These models leveraged the power of deep learning to capture the complexities of natural language and database schemas, leading to more accurate and robust Text2SQL systems.\\n\\nFurthermore, the integration of large language models (LLMs) like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) into Text2SQL further pushed the boundaries of performance. These pre-trained models, fine-tuned on Text2SQL tasks, demonstrated superior understanding of language semantics and context, resulting in more accurate query generation. For instance, Grappa (Yu et al., 2020) combined grammar-augmented pre-training with table semantic parsing, showcasing the potential of LLMs in Text2SQL.\\n\\nThe deep learning era also witnessed the emergence of interactive Text2SQL systems, which aimed to address the ambiguity inherent in natural language queries. These systems, such as the one proposed by Li et al. (2020), employed parser-independent interactive approaches to enhance query understanding and disambiguation. By engaging users in a step-by-step dialogue, these systems could clarify ambiguities and generate more accurate SQL queries.\\n\\nIn summary, the deep learning era marked a significant leap forward in Text2SQL technology. The integration of neural networks, LLMs, and interactive systems revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.',\n",
       " '\\n## 2.3 Large Language Models\\n\\nThe integration of large language models (LLMs) into Text2SQL has significantly advanced the field. LLMs, such as BERT (Devlin et al., 2019) and GPT (Radford et al., 2018), have demonstrated remarkable capabilities in understanding language semantics and context, leading to more accurate and robust query generation. Fine-tuning these pre-trained models on Text2SQL tasks has proven to be highly effective, as evidenced by the success of models like Grappa (Yu et al., 2020), which combines grammar-augmented pre-training with table semantic parsing. The use of LLMs has also enabled the development of more user-friendly and interactive Text2SQL systems, which can better handle the ambiguities inherent in natural language queries. For example, the system proposed by Li et al. (2020) employs a parser-independent interactive approach to enhance query understanding and disambiguation through step-by-step dialogue with the user. Overall, the integration of LLMs into Text2SQL has opened up new avenues for research and development, paving the way for more sophisticated and powerful natural language interfaces to databases.',\n",
       " \"\\n## 2.4 Data Augmentation\\n\\nData augmentation plays a crucial role in enhancing the performance and generalization capabilities of Text2SQL models. Given the limited availability of labeled data for specific databases, techniques for synthesizing parallel datasets have gained significant attention. For example, the Curated LLM: Synergy of LLMs and Data Curation for Tabular Augmentation in Low-Data Regimes paper discusses synthetic data generation to augment datasets in low-data regimes . Additionally, the Label-Guided Generative Adversarial Network for Realistic Image Synthesis paper explores the generation of realistic images from labels, which is valuable for dataset synthesis . Furthermore, the Generalized Large-Scale Data Condensation Via Various Backbone and Statistical Matching paper introduces generalized backbone matching and statistical matching for data synthesis .\\n\\nOne notable approach is the REFILL framework (Awasthi et al., 2023), which retrieves and edits text queries from existing schemas to generate diverse parallel datasets for adapting Text2SQL parsers to new schemas. By leveraging parallel datasets from multiple existing schemas, REFILL retrieves diverse text queries paired with SQLs structurally similar to the target workload. REFILL learns to retrieve-and-edit text queries from the existing schemas and transfers them to the target schema. We show that retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the target schema, leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation methods. Through experiments spanning multiple databases, we demonstrate that fine-tuning parsers on datasets synthesized using REFILL consistently outperforms the prior data-augmentation methods. REFILL leverages parallel datasets from several existing schemas, such as Spider ( Yu et al. ,2018 ), to first retrieve a diverse set of text paired with SQLs that are structurally similar to a given SQL q (§ 2.1 ). Then, it trains a novel schema translator model for converting the text of the training schema to the target schema of q . The schema translator is decomposed into a mask and fill step to facilitate training without direct parallel examples of schema translation. Our design of the mask module and our method of creating labeled data for the fill module entails non-trivial details that we explain in this paper (§ 2.2). REFILL also incorporates a method of filtering-out inconsistent (Text,SQL) pairs using an independent binary classifier (§ 2.3), that provides more useful quality scores, than the cycle-consistency based filtering ( Zhong et al. ,2020 ). Our approach is related to retrieve-and-edit models that have been used for semantic parsing ( Hashimoto et al. ,2018 ), dialogue generation ( Chi et al. ,2021 ), translation ( Cai et al. ,2021 ), and question answering ( Karpukhin et al. ,2020 ). However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work. We propose a framework called REFILL (§ 2) for generating diverse text queries for a given SQL workload that is often readily available ( Baik et al. ,2019 ). REFILL leverages parallel datasets from several existing schemas, such as Spider ( Yu et al. ,2018 ), to first retrieve a diverse set of text paired with SQLs that are structurally similar to a given SQL q (§ 2.1 ). Then, it trains a novel schema translator model for converting the text of the training schema to the target schema of q . The schema translator is decomposed into a mask and fill step to facilitate training without direct parallel examples of schema translation. Our design of the mask module and our method of creating labeled data for the fill module entails non-trivial details that we explain in this paper (§ 2.2). REFILL also incorporates a method of filtering-out inconsistent (Text,SQL) pairs using an independent binary classifier (§ 2.3), that provides more useful quality scores, than the cycle-consistency based filtering ( Zhong et al. ,2020 ). Our approach is related to retrieve-and-edit models that have been used for semantic parsing ( Hashimoto et al. ,2018 ), dialogue generation ( Chi et al. ,2021 ), translation ( Cai et al. ,2021 ), and question answering ( Karpukhin et al. ,2020 ). However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work. We summarize our contributions as follows: (i) We propose the idea of retrieving and editing natural text from several existing schemas for transferring it to a target schema, obtaining higher text diversity compared to the standard SQL-to-Text generators. (ii) We design strategies for masking schema-specific words in the retrieved text and training the REFILL model to fill in the masked positions with words relevant to the target schema. (iii) We filter high-quality parallel data using a binary classifier and show that it is more efficient than existing methods based on cycle-consistency filtering. (iv) We compare REFILL with prior data-augmentation methods across multiple schemas and consistently observe that fine-tuning Text-to-SQL parsers on data generated by REFILL leads to more accurate adaptation.\\n\\nIt then employs a schema translator model to convert the text of the training schema to the target schema, facilitating adaptation to new databases. This method demonstrates consistent performance improvements over prior data augmentation techniques, highlighting the effectiveness of data-driven approaches for enhancing Text2SQL model adaptability.\\n\\nAnother relevant work is the study by Zhao et al. (2022), which emphasizes the importance of synthesizing high-quality data for Text2SQL parsing. Their findings underscore the need for diverse and representative training data to achieve optimal performance and generalization. For example, a network trained for accelerated magnetic resonance imaging (MRI) on one scanner performs worse on another scanner. Models trained on the combination of various data distributions, such as those obtained from different MRI scanners and anatomies, exhibit robustness equal or superior to models trained on the best single distribution for a specific target distribution. Thus training on diverse data tends to improve robustness. Furthermore, training on diverse data does not compromise in-distribution performance, i.e., a model trained on diverse data yields in-distribution performance at least as good as models trained on the more narrow individual distributions. Our results suggest that training a model for imaging on a variety of distributions tends to yield a more effective and robust model than maintaining separate models for individual distributions.\\n\\nBy incorporating techniques like data augmentation and synthetic data generation, researchers can effectively address the data scarcity challenge and improve the robustness of Text2SQL models. For instance, the study 'Real-Fake: Effective Training Data Synthesis Through Distribution Matching' demonstrates that augmenting real data with synthetic data can lead to performance improvements across various benchmarks, with boosts of $2.1\\\\\\\\%$ and $1.9\\\\\\\\%$ on IN-10 and IN-100 datasets respectively . Additionally, the research in 'Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries' introduces a dataset that incorporates advanced data analysis and unclear queries, which can be beneficial for training more robust Text2SQL models .\\n\\nIn conclusion, data augmentation techniques have emerged as a vital component in advancing Text2SQL technology. By synthesizing parallel datasets and leveraging large language models, researchers can enhance the adaptability and generalization capabilities of Text2SQL models, paving the way for more accurate and efficient natural language interfaces to databases.\",\n",
       " '\\n## 2.5 Addressing Ambiguity\\n\\nAmbiguity in natural language queries poses a significant challenge for Text2SQL systems. For instance, the AmbiQT benchmark, which includes over 3000 examples where each text is interpretable as two plausible SQLs due to lexical and/or structural ambiguity, highlights this issue. This ambiguity can arise from various sources such as overlapping schema names, multiple confusing relationship paths, and the inherent ambiguity of natural language. Furthermore, current Text-to-SQL systems and decoding algorithms, including those employing state-of-the-art LLMs, struggle to generate all valid interpretations for possible disambiguation by the user. Users may express their information needs in various ways, leading to multiple valid interpretations and corresponding SQL queries. Addressing this ambiguity is crucial for achieving accurate and robust query generation.\\n\\nOne approach to handling ambiguity is through interactive systems that engage users in a step-by-step dialogue to clarify their intent. For example, the work by Stengel-Eskin et al. (2023) introduces A MP, a framework, dataset, and challenge for translating ambiguous natural language to formal representations like logic and code, which can be used in interactive systems to handle ambiguity. Additionally, the study by Zhao et al. (2021) proposes a generation system that addresses the cold-start zero-shot clarifying question challenge in conversational search, which is another example of interactive systems that engage users in a step-by-step dialogue to clarify their intent. Furthermore, the research by Qian et al. (2022) focuses on resolving ambiguities in text-to-image generative models through a disambiguation framework that engages users in a step-by-step dialogue to clarify their intent. For instance, the system proposed by Li et al. (2020) employs a parser-independent interactive approach, allowing users to refine their queries based on feedback and disambiguate potential misunderstandings. This interactive process enhances query understanding and improves the accuracy of the generated SQL queries.\\n\\nAnother technique for addressing ambiguity is the use of disambiguation techniques within the model itself. For instance, word sense disambiguation is one of the areas in NLP that has gained significant attention and numerous works have been proposed in this regards ( Wang and Wang ,2021 ). Resolving ambiguities in question answering ( Min et al. ,2020 ), conversational question answering ( Guo et al. ,2021 ), and task-oriented dialogue systems ( Qian et al. ,2022 ) has also been previously studied. Ambiguity resolution has also been studied in multi-modal applications, such as multi-modal machine translation ( Li et al. ,2022 )or matching images or videos to disambiguated interpretation of a sentence ( Berzak et al. ,2015 ). Despite those recent efforts, not much attention has been paid to ambiguities in text-to-image generative models. On the other hand, the growing popularity of those models, both in academic and non-academic circles, make it imperatives to better understand potential issues with those systems due to language ambiguity. In this paper we have identified and addressed some of those issues. We hope that our work will inspire future effort on this important problem. For example, the AmbiQT benchmark (Wang et al., 2022) introduces a dataset with ambiguous queries, each having two distinct valid SQL interpretations. The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs. Inspired by our experience with several real-world datasets, we target four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity. This benchmark encourages the development of Text2SQL models capable of handling ambiguity by considering multiple interpretations and ranking them based on their relevance to the query.\\n\\nFurthermore, the work by Pourreza and Rafiei (2023) highlights the importance of cautious interpretation of benchmark evaluations. They demonstrate that achieving perfect performance on existing benchmarks is unfeasible due to the inherent ambiguity in natural language queries. Their evaluation reveals that the true performance of Text2SQL models may be underestimated, emphasizing the need for additional independent evaluations and the consideration of multiple valid interpretations in benchmark design.\\n\\nIn conclusion, addressing ambiguity in Text2SQL remains an active area of research. Ambiguity in SQL has been studied in other fields of NLP, but it has been unexplored in the context of semantic parsing. AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion. Interactive systems, disambiguation techniques, and careful interpretation of benchmark evaluations are essential for developing accurate and robust Text2SQL models capable of handling the complexities of natural language queries.',\n",
       " '\\n## 2.6 Ethical Considerations\\n\\nThe ethical implications of Text2SQL technology cannot be overlooked, especially considering its potential applications in sensitive domains like healthcare, finance, and government. While Text2SQL systems offer immense benefits by democratizing access to data, they also raise concerns regarding data privacy, fairness, and potential biases inherited from training data. For instance, systems trained on large-scale unfiltered data can suffer from degenerated and biased behavior, which can reflect and reinforce societal biases and structural inequalities. Additionally, the risks associated with neural rendering studies, such as privacy and security issues linked to the capture of sensitive information, are also relevant to Text2SQL systems. Furthermore, the potential negative impact on society from generating editable 3D shapes without 3D supervision, such as the generation of deep fakes, is a concern that must be addressed.\\n\\nRecent studies, such as the work by Liu et al. (2023), have uncovered social biases in Text2SQL models, highlighting the need for careful consideration of the potential consequences of deploying these systems in real-world applications. Text-to-SQL models bridge the gap between database manipulation and amateur users and are mainly applied by administrative industries, such as banks, schools, and governments, which rely on AI-based applications to manipulate databases and further develop policies that will have profound impacts on various aspects of many people’s lives. If there are unwanted prejudices against specific demographics in applied Text-to-SQL models, these stereotypes can be significantly amplified since their retrieval results are adopted by administrative industries to draft policies. Unfortunately, large pre-trained language models (PLMs) are actually acknowledged to contain social biases towards different demographics, and these wicked biases are observed to be inherited by downstream tasks. Some may suppose that these harmful biases could be forgotten or mitigated when fine-tuned on downstream neutral data that does not contain any toxic words, specific demographic keywords, or any judgmental expressions. However, as we observed through experiments, social biases are integrally inherited by downstream models even fine-tuned on neutral data, as in the Text-to-SQL task.\\n\\nThese biases can manifest in various forms, including stereotypical correlations between judgmental expressions and different demographics, as well as incorrect comparisons that perpetuate harmful stereotypes. For instance, the words associated with unmarked, White GPT-3.5 personas include neutral, everyday descriptions, such as good, while those associated with other groups tend not to (Table 3). Similarly, friendly and casually are top words for man personas. On the other hand, generated personas of marked groups reproduce problematic archetypes. Middle-Eastern personas disproportionately mention religion (faith, religious, headscarf). This conflation of Middle-Eastern identity with religious piety—and specifically the conflation of Arab with Muslim—has been criticized by media scholars for dehumanizing and demonizing Middle-Eastern people as brutal religious fanatics (Muscati, 2002; Shaheen, 2003). Also, the words differentiating several marked race/ethnic groups from the default one (White) include culture, traditional, proud, and heritage. These patterns align with previous findings that those in marked groups are defined primarily by their relationship to their demographic identity, which continues to set these groups apart in contrast to the default of whiteness (Frankenburg, 1993; Pierre, 2004; Lewis, 2004). Similarly, the words for nonbinary personas, such as gender, identity, norms, and expectations, exclusively focus on the portrayed individual’s relationship to their gender identity. The words for Middle-Eastern and Asian personas connect to critiques of Orientalism, a damaging depiction where the East (encompassing Asia and the Middle East) is represented as the “ultimate Other” against which Western culture is defined; inaccurate, romanticized representations of these cultures have historically been used as implicit justification for imperialism in these areas (Said, 1978; Ma, 2000; Yoshihara, 2002). By pigeonholing particular demographic groups into specific narratives, the patterns in these generations homogenize these groups rather than characterizing the diversity within them. This reflects essentialism: individuals in these groups are defined solely by a limited, seemingly-fixed essential set of characteristics rather than their full humanity (Rosenblum and Travis, 1996; Woodward, 1997). Essentializing portrayals foster the othering of marked groups, further entrenching their difference from the default groups of society (Brekhus, 1998; Jensen, 2011; Dervin, 2012).\\n\\nTo address these concerns, researchers have proposed several approaches. The BiaSpider benchmark (Liu et al., 2023) aims to uncover and categorize social biases in Text2SQL models by introducing a new paradigm for structured data bias measurement  . This benchmark provides a valuable tool for evaluating and mitigating biases in Text2SQL systems.\\n\\nAdditionally, the work by Awasthi et al. (2023) emphasizes the importance of reviewing Text2SQL systems for harmful biases before deployment and ensuring that users are aware of the potential for incorrect answers . This highlights the need for responsible development and deployment of Text2SQL technology, with a focus on fairness, transparency, and accountability.\\n\\nIn conclusion, while Text2SQL technology offers significant benefits, it is crucial to address the ethical considerations associated with its use. The integration of ChatGPT in studies carries ethical implications with broad social ramifications. It enables inclusive communication but raises concerns about misinformation and biases. Ethical considerations demand transparency, bias mitigation, and ongoing evaluation to harness its benefits responsibly. Our goal with REFILL is to synthesize parallel data for adapting Text-to-SQL parsers to new schemas. We believe that the real-world deployment of Text-to-SQL or any semantic parser trained on text generated by language models must go through a careful review of any harmful biases. Also, the intended users of any Text-to-SQL service must be made aware that the answers generated by these systems are likely to be incorrect.',\n",
       " '\\n## 3 Current Benchmarks and Models',\n",
       " \"\\n## 3.1 Benchmarks\\n\\nThe evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al. ,2018 ), SPIDER ( Yu et al. ,2018 ), KaggleDBQA ( Lee et al. ,2021 ), SEDE ( Hazoom et al. ,2021 ), and EHRSQL ( Lee et al. ,2022 ). These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets. Dr. SPIDER ( Chang et al. ,2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER. Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges. For instance, WikiSQL ( Zhong et al. ,2018 )and SPIDER ( Yu et al. ,2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al. ,2021 ), SEDE ( Hazoom et al. ,2021 ), and EHRSQL ( Lee et al. ,2022 ) aim to capture real-world scenarios. Dr. SPIDER ( Chang et al. ,2023 ) tests the robustness of existing models by perturbing either the text or schema. The AmbiQT benchmark ( Wang et al. ,2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion. Additionally, Text2Analysis ( He et al. ,2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis. These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities. \\n\\nOne of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database. WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models. However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios. For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35% . Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios .\\n\\nTo address this limitation, the Spider benchmark (Yu et al., 2018) was introduced. Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas. Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.\\n\\nAnother notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context. This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical. Each domain contains millions of passages and sufficient human-annotated query-passage related pairs, collected from real search engine systems within Alibaba Group. The authenticity of the samples allows SParC to meet the needs of both academia and industry fields, pushing forward the quality and variety of Chinese passage retrieval datasets. SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions. This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.\\n\\nAmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates. The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation. It consists of natural language questions with multiple valid SQL interpretations, each representing a different interpretation of the user\\\\'s intent. AmbiQT challenges Text2SQL models to handle ambiguity and rank multiple interpretations based on their relevance to the query. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, targeting four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity. When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution. We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling. Most outputs are small lexical tweaks of the top choice, bringing about little meaningful diversity in SQL structures or schema alternatives. Even SOTA LLMs like ChatGPT suffer from this issue.\\n\\nWikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions. WikiSQL ( Zhong et al. ,2018 ) and SPIDER ( Yu et al. ,2018 ) are popular benchmarks for the Textto-SQL task, while AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives. AmbiQT is constructed so that each text query has two distinct valid SQL interpretations, encompassing four types of ambiguity: Column Ambiguity (C), Table Ambiguity (T), Join Ambiguity (J), and Precomputed Aggregates (P). This benchmark tests performance under ambiguity in the context of current models, addressing the lack of evaluation of Text-to-SQL models under ambiguity in contemporary literature.\\n\\nThese benchmarks continue to play a crucial role in driving advancements in Text2SQL technology and ensuring the development of accurate and robust natural language interfaces to databases.\\n\\n### 3.2 Models\\n\\nThe landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning. This subsection delves into the key models that have shaped the current state of Text2SQL technology.\\n\\n**Sequence-to-Sequence Models:**\\n\\nOne of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model. This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries. The encoder processes the input question and encodes it into a fixed-length vector, while the decoder decodes this vector into the target SQL query. Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a). These models demonstrated the effectiveness of neural networks in capturing the intricacies of natural language and database schemas, laying the foundation for subsequent advancements.\\n\\n**Graph-Based Models:**\\n\\nGraph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas. These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation. Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema. These models have shown promising results in handling complex queries and improving the accuracy of generated SQL queries.\\n\\n**Hybrid Models:**\\n\\nHybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths. These models often employ a sequence-to-sequence architecture for the overall query generation process while incorporating graph-based components to handle schema reasoning and complex relationships. An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.\\n\\n**Large Language Models (LLMs):**\\n\\nThe integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness. Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy. Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.\\n\\n**Interactive Systems:**\\n\\nInteractive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries. These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings. Examples of interactive systems include the one proposed by Li et al. (2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.\\n\\nIn conclusion, the current landscape of Text2SQL models is diverse and evolving, with various architectures and approaches being explored to tackle the challenges of natural language understanding and database schema reasoning. Sequence-to-sequence models, graph-based models, hybrid models, LLMs, and interactive systems each contribute to the advancement of Text2SQL technology, paving the way for more accurate and user-friendly natural language interfaces to databases.\",\n",
       " '\\n## 4 Limitations and Future Directions',\n",
       " \"\\n## 4.1 Evaluation Metrics\\n\\nThe evaluation of Text2SQL models is crucial for assessing their performance and driving further research and development. This is evident in the Text2Analysis benchmark, which addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis, providing a comprehensive taxonomy of advanced analysis and unclear queries, which enables the evaluation of the analytical abilities of large language models. Additionally, the evaluation of five state-of-the-art models on the Text2Analysis dataset reveals their strengths and weaknesses in handling advanced analysis tasks and unclear queries, providing valuable insights for future research. However, the current evaluation metrics have limitations that need to be addressed to ensure a comprehensive and accurate assessment of model capabilities. For instance, comparisons are limited to publicly available checkpoints, which can lead to significant confounding variables due to differences in training recipes and datasets. Additionally, the focus on specific aspects of 3D awareness, such as single-image surface reconstruction and multiview consistency, may not provide a comprehensive understanding of a model's 3D capabilities. Furthermore, the reliance on probing methods like linear probes and zero-shot analysis may not fully capture the model's ability to adapt to 3D tasks.\\n\\n**Exact Set Match Accuracy:** This metric measures the percentage of model-generated SQL queries that exactly match the reference SQL queries in the benchmark. While it provides a straightforward measure of accuracy, it fails to capture the nuances of SQL queries that may be semantically equivalent but differ in syntactic structure. For example, the evaluation of table exact match accuracy treats partial matches as incorrect, which may not be ideal for queries that do not impose ordering among columns or rows. Additionally, the reliance on lexical match to measure model effectiveness may not fully capture the underlying meaning of paraphrased sequences, as tables store factual information in an ordered manner. This limitation can lead to underestimating the true performance of Text2SQL models, as demonstrated by Pourreza and Rafiei (2023).\\n\\n**Execution Accuracy:** Execution accuracy evaluates the percentage of model-generated SQL queries that produce the same output as the reference queries when executed against the database. While this metric addresses the limitations of exact set match accuracy by considering the query results, it still has its drawbacks. It assumes that the reference queries are error-free and may not account for alternative valid queries that could also produce correct results. Additionally, execution accuracy can be affected by ties in the database, where multiple rows satisfy the query conditions, leading to potential discrepancies in the evaluation results. For example, when a query asks for the top rows that satisfy certain conditions, such as the student with the highest GPA or the youngest student, and there is a tie for the top position, the corresponding SQL query may return all ties or only one. This becomes a problem in evaluation if a model-generated query and the reference query treat the ties differently. Furthermore, the use of the LIMIT n clause in SQL queries can also lead to ties, particularly when there is a tie on row n with multiple rows having the same values. The ordering among tied rows can vary between two queries, and so is the first n rows that are returned. Another issue arises from the incorrect usage of non-aggregated columns in both the SELECT clause and the GROUP BY clause, which can result in multiple records being associated with the same grouping column or aggregation value, whereas each group can only return one record. These ties and ambiguities can lead to discrepancies in the evaluation results and affect the execution accuracy of text-to-SQL models.\\n\\n**Limitations and Potential Improvements:** To address the limitations of current evaluation metrics, several potential improvements can be considered. First, incorporating semantic equivalence checks that go beyond syntactic matching can provide a more accurate assessment of query correctness. Semantic entropy improves over baselines in predicting whether a model’s answer to a question is correct. This can be achieved by leveraging techniques like query rewriting and normalization to identify semantically equivalent queries. Query rewriting aims to train a rewriting model to mimic human-rewritten queries, which can solve ambiguous problems and recover missing elements from the context. Query expansion methods, such as selecting terms via the normalization score of their embeddings, can also enhance search queries and produce better retrieval results. Integrating both query rewriting and query expansion can reformulate better conversational queries. Second, incorporating multiple reference queries for each natural language question can account for the inherent ambiguity in natural language and provide a more comprehensive evaluation of model performance. For instance, the AmbigQA dataset measures a model’s ability to disambiguate-and-answer ambiguous questions, such as determining the specific game in the 'Fallout' series being referred to in a query like “Where does the new fallout game take place?” and then providing the correct location, “Appalachia”. Furthermore, SituatedQA focuses on temporal and geographic ambiguity, where additional time ranges and their corresponding answers are crowdsourced, and geographic questions are created by removing references to location and then crowdsourcing locations and corresponding answers. These datasets demonstrate the importance of accounting for ambiguity in natural language questions to improve model performance and calibration. Third, developing evaluation metrics that consider the diversity of generated queries and their relevance to the user's intent can provide a more nuanced understanding of model capabilities. For example, in the context of multimodal fusion, it has been observed that increased data diversity can lead to substantial improvements in performance, especially in scarce data regimes. Furthermore, fine-grained evaluation tests can be designed to assess specific model capabilities, such as understanding of ontology, logical equivalence, and answering under visual obfuscation.\\n\\nIn conclusion, while current evaluation metrics have played a crucial role in assessing Text2SQL models, their limitations need to be addressed to ensure a more accurate and comprehensive evaluation. For instance, denotation accuracy, widely used in semantic parsing, is not directly applicable to tasks where tabular input encoding, reasoning, and generation are performed by the same model. Additionally, the strict binary measure of table exact match may not be ideal for queries that do not impose ordering among columns or rows. Furthermore, the limitations in training and benchmarking, as well as the need for more diverse and larger human evaluation, highlight the importance of exploring more evaluation approaches and metrics. By incorporating semantic equivalence checks, considering multiple reference queries, and evaluating query diversity and relevance, we can drive further advancements in Text2SQL technology and develop more robust and accurate natural language interfaces to databases.\\n\\nTo address the limitations of current evaluation metrics, several potential improvements can be considered. For instance, in the context of spatio-temporal prediction across diverse disciplines, limitations such as training limitations, benchmark limitations, and evaluation limitations have been identified. Training limitations include the constraint on model architecture and size, which may be improved by exploring specific architecture enhancements or larger models. Benchmark limitations involve the scope of methods included and the calibration of dataset protocols, suggesting a need for a wider method spectrum and further work on aspects like the impact of the number of input frames. Evaluation limitations highlight the need for a more diverse and larger pool of participants in human evaluations, as well as the exploration of additional evaluation approaches and metrics for a more holistic assessment of models. These insights are drawn from studies that have examined the prevalent methods, representative datasets, and powerful benchmarks in the field, acknowledging that while progress has been made, there is still much to be done to refine evaluation metrics.\",\n",
       " '\\n## 4.2 Combining Text2SQL with Other Tasks\\n\\nCombining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases. By integrating Text2SQL with tasks like question answering, information extraction, and natural language generation, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.\\n\\nOne potential area of integration is with question answering (QA) systems. By combining Text2SQL with QA, we can build systems that can not only translate natural language questions into SQL queries but also answer those questions directly using the retrieved data. This integration can be achieved by incorporating QA models into the Text2SQL pipeline, allowing the system to generate natural language answers based on the results of the executed SQL queries. This approach can provide a more user-friendly and intuitive interface for interacting with databases, as users can pose questions in natural language and receive answers in a similar format.\\n\\nAnother area of integration is with information extraction (IE) tasks. By combining Text2SQL with IE, we can build systems that can extract structured information from unstructured text sources and store it in databases. This integration can be achieved by incorporating IE models into the Text2SQL pipeline, allowing the system to extract relevant information from text sources and generate SQL queries to insert or update the extracted data in the database. For instance, UniEX: an Effective and Efficient Framework for Unified Information Extraction Via a Span-extractive Perspective () demonstrates the potential of using a unified extractive framework for various IE tasks, which can be beneficial for the Text2SQL pipeline. Additionally, the work on Benchmarking and Improving Text-to-SQL Generation under Ambiguity () highlights the importance of addressing ambiguity in SQL generation, a critical aspect when integrating IE models into the Text2SQL process. This approach can facilitate the automated creation and maintenance of databases, as well as enable more sophisticated data analysis and retrieval tasks.\\n\\nFurthermore, integrating Text2SQL with natural language generation (NLG) tasks can enable the generation of natural language explanations and summaries of query results. This can enhance the interpretability and accessibility of query results, making it easier for users to understand and analyze the retrieved data. For example, the system proposed by Kokkalis et al. (2012) translates SQL queries into narratives, providing users with a more intuitive understanding of the query results.\\n\\nIn conclusion, combining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases. By integrating Text2SQL with QA, IE, and NLG tasks, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences. This integration can lead to the development of more user-friendly, efficient, and intelligent natural language interfaces to databases, empowering users to access and analyze data more effectively.',\n",
       " \"\\n## 4.3 Addressing Bias\\n\\nAddressing bias in Text2SQL systems is of paramount importance, especially considering their potential applications in sensitive domains like healthcare, finance, and government.  Biased Text2SQL models can perpetuate and amplify existing stereotypes, leading to unfair and discriminatory outcomes.  Therefore, it is crucial to develop methods for identifying, mitigating, and eliminating bias in these systems.\\n\\nOne approach to addressing bias is through the use of diverse and representative training data.  By ensuring that the training data encompasses a wide range of perspectives and demographics, we can reduce the likelihood of biased model predictions. Techniques like data augmentation and synthetic data generation can be employed to create more diverse training datasets and improve the generalizability of Text2SQL models. \\n\\nAnother important strategy is to incorporate bias mitigation techniques during the model development process.  This can involve using techniques like adversarial training, which aims to minimize the model's reliance on biased features, or incorporating fairness constraints into the training objective. These techniques can help ensure that the model treats different demographic groups fairly and avoids perpetuating harmful stereotypes. \\n\\nFurthermore, it is crucial to evaluate Text2SQL models for bias and fairness before deployment.  This can be achieved through the use of bias detection tools and fairness metrics, which can help identify potential biases in the model's predictions. By carefully evaluating and addressing bias, we can ensure that Text2SQL systems are fair, transparent, and accountable. \\n\\nIn conclusion, addressing bias in Text2SQL systems is an essential step towards building fair and responsible natural language interfaces to databases.  By incorporating diverse training data, bias mitigation techniques, and rigorous evaluation procedures, we can develop Text2SQL models that are not only accurate and efficient but also ethical and trustworthy.\",\n",
       " \"\\n## 4.4 Future Research Directions\\n\\nThe field of Text2SQL is rapidly evolving, with numerous opportunities for future research and development. This subsection explores several promising directions that can further advance Text2SQL technology and broaden its applications.\\n\\n**Advanced NLP Techniques:**\\n\\nIntegrating more advanced NLP techniques into Text2SQL models can significantly enhance their understanding of natural language and improve query generation accuracy. For instance, the use of chain-of-thought prompting has been shown to improve performance on text-to-SQL parsing tasks, as demonstrated by the question-decomposition prompting method (QDecomp) which outperforms existing prompting methods by 2.4 and 1.5 point absolute gains on the development set of Spider and Spider Realistic datasets. For instance, incorporating techniques like dependency parsing, coreference resolution, and semantic role labeling can help models better capture the relationships between different entities in the query and generate more accurate SQL queries. Exploring the use of transformer-based models with attention mechanisms can also enable models to better handle long-range dependencies and complex sentence structures.\\n\\n**Combining Text2SQL with Other Tasks:**\\n\\nCombining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems. For example, integrating Text2SQL with question answering systems can enable users to pose questions in natural language and receive answers directly without the need for intermediate SQL queries. Combining Text2SQL with information extraction tasks can facilitate the automated creation and maintenance of databases by extracting structured information from unstructured text sources. Integrating Text2SQL with natural language generation tasks can enable the generation of natural language explanations and summaries of query results, enhancing interpretability and accessibility.\\n\\n**Addressing Real-world Challenges:**\\n\\nDeveloping Text2SQL systems that can handle real-world challenges like ambiguity, noise, and domain-specific language is crucial for practical applications. Ambiguity in SQL, arising from related column names, has been studied in (Wang et al., 2022), but they only consider column ambiguity. Their method of recognizing ambiguous queries depends on labeling words of the text and does not generalize to other kinds of ambiguity. To the best of our discernment, AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives. AmbiQT is constructed so that each text query has two distinct valid SQL interpretations. Motivated by our experience working with real-life databases, we designed AmbiQT to encompass four types of ambiguity: Column Ambiguity (C), Table Ambiguity (T), Join Ambiguity (J), and Precomputed Aggregates (P). Each entry is designed so that both alternatives have a similar relevance to the question, and a well-calibrated decoding method is expected to rank them close by in their outputs. Exploring techniques like interactive systems, disambiguation methods, and domain adaptation can help models better handle these challenges and improve their performance in real-world scenarios. For instance, domain adaptation techniques have been shown to improve generalization performance by simulating domain shift through a training procedure that divides the source domain into meta-train and meta-test domains. Additionally, disentangled representation learning can disentangle features into a domain-invariant content space and a domain-specific attribute space, thus learning a domain-invariant representation from data across multiple domains. Furthermore, recent studies demonstrate that pre-trained models can bring out-of-distribution generalization capabilities. Additionally, investigating the use of transfer learning and few-shot learning can enable models to quickly adapt to new domains and tasks with limited training data. For example, the best model performs less than $30\\\\\\\\%$ accuracy for the 5- shot setting on the most difficult ChestX dataset [56]. This reveals that common knowledge like ImageNet [52] can only provide a reasonable distribution for initialization, but it is very hard to learn the real expert knowledge in some medical applications. Moreover, the setting we explored is still under the $N$ -way $K$ -shot learning system, while the real-world demands often require an adaptive $X$ -way or $Y$ -shot for both learning and inference, which should also be explored in future work. We believe that this could be solved by learning adaptive reprojections and alignment strategies which are highly related to input instances.\\n\\n**Ethical Considerations and Bias Mitigation:**\\n\\nContinuing to address ethical considerations and bias mitigation in Text2SQL systems is essential for building fair and responsible natural language interfaces to databases. This involves incorporating diverse and representative training data, bias mitigation techniques, and rigorous evaluation procedures to ensure that Text2SQL models are not only accurate and efficient but also ethical and trustworthy. This involves incorporating diverse and representative training data, bias mitigation techniques, and rigorous evaluation procedures to ensure that Text2SQL models are not only accurate and efficient but also ethical and trustworthy.\\n\\nIn conclusion, the future of Text2SQL is bright, with numerous opportunities for research and development. By exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations, we can further advance Text2SQL technology and unlock its full potential for empowering users to access and analyze data more effectively. \\n\\nCombining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems. For example, the integration of Text2SQL with interactive semantic parsing for SQL allows users to validate and refine generated queries through step-by-step explanations, enhancing the overall system's performance and user experience. Additionally, incorporating Text2SQL with natural language explanations for SQL queries can improve the accessibility and interpretability of the system. Furthermore, the combination of Text2SQL with retrieval enhancement techniques can generate more diverse and accurate text, increasing the system's versatility. Finally, the integration of Text2SQL with human-in-the-loop approaches can facilitate the generation of high-quality data with accurate diversification, further enhancing the system's capabilities.\",\n",
       " \"\\n## 5 Conclusion\\n\\nThe Text2SQL task has seen significant advancements in recent years, driven by the integration of deep learning, large language models, and interactive systems. This research survey has provided a comprehensive overview of the current state of Text2SQL technology, exploring its evolution, key benchmarks and models, limitations, and future directions. The Text2Analysis benchmark is proposed as a new benchmark to further explore LLMs’ upper limits in challenging tabular data analysis tasks . We have presented the Text2Analysis dataset that addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis . A Text-to-SQL model takes as input a question expressed as a natural language text x, and a database schema comprising of table and column names, and outputs an SQL program y which can be executed against the database to answer the user’s question . In this paper, we propose to uncover and categorize social biases in the Text-to-SQL task .\\n\\nThe survey began by discussing the background and related work, highlighting the early developments in Text2SQL, the impact of deep learning and large language models, and techniques for data augmentation and ambiguity handling. It then delved into the current benchmarks and models, analyzing popular benchmarks like WikiSQL and Spider, and examining different Text2SQL models, including sequence-to-sequence models, graph-based models, and hybrid models. The survey continued by discussing the limitations of current Text2SQL systems and proposing potential solutions and future research directions. This included a critical analysis of evaluation metrics, the potential for combining Text2SQL with other NLP tasks, methods for addressing bias, and new research directions for advancing Text2SQL technology.\\n\\nThe survey concludes by summarizing the key findings and highlighting the potential impact of Text2SQL technology. The integration of neural networks, LLMs, and interactive systems has revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems. However, challenges remain in addressing ambiguity, bias, and real-world complexities. For instance, preferences and values are not universal, and they are often inconsistently defined . Additionally, human feedback is inherently incomplete, and operationalizing a 'good' output is difficult . Furthermore, crowdworkers and social media users are neither representative nor sufficient, which can lead to biased outcomes . Future research directions include exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations. By continuing to advance Text2SQL technology, we can unlock its full potential for empowering users to access and analyze data more effectively.\"]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "from research_agent.core.general_llm import LLM\n",
    "from research_agent.core.config import Config\n",
    "from pyaml_env import parse_config\n",
    "configs = parse_config(Config.YAML_CONFIG)\n",
    "llm = LLM(config=configs[Config.DEFAULT_MODEL])\n",
    "\n",
    "import json\n",
    "from typing import List\n",
    "from pathlib import Path\n",
    "\n",
    "import json_repair\n",
    "from jinja2 import Environment\n",
    "from research_agent.core.query import Query\n",
    "from research_agent.core.general_llm import LLM\n",
    "from research_agent.core.config import Config\n",
    "from pyaml_env import parse_config\n",
    "from nltk.tokenize  import sent_tokenize\n",
    "\n",
    "class FindStatementCitation:\n",
    "    def __init__(self):\n",
    "        # 解析配置文件，加载默认模型配置\n",
    "        configs = parse_config(Config.YAML_CONFIG)\n",
    "        self.llm = LLM(config=configs[Config.DEFAULT_MODEL])\n",
    "        self.query = Query()\n",
    "\n",
    "        # 获取当前文件所在目录的路径并找到 prompts 文件夹\n",
    "        base_path = r\"D:\\GoodStudy\\FX15\\FX15H\\final_work\\FX15_research_agent\\summary-generation-match\\research_agent\\core\\prompts\"\n",
    "\n",
    "        # 修改文件路径的获取方式，加载用于查找语句引用的 Jinja 模板\n",
    "        find_statement_citation_prompt_file = base_path + r\"\\find_statements.jinja\"\n",
    "        with open(find_statement_citation_prompt_file, \"r\", encoding=\"utf-8\") as f:\n",
    "            # 使用 Jinja2 环境加载模板\n",
    "            self.find_statement_citation_prompt_template = Environment().from_string(f.read())\n",
    "\n",
    "    async def find_statement_citation(\n",
    "        self, topic: str, section: str\n",
    "    ):\n",
    "        \"\"\"\n",
    "        调用模型生成对给定主题和调查草稿的回答，查找相关的语句引用\n",
    "\n",
    "        Args:\n",
    "            topic: 研究的主题\n",
    "            section: 调查草稿内容\n",
    "\n",
    "        Returns:\n",
    "            response[\"statements\"]: 模型返回的引用语句\n",
    "        \"\"\"\n",
    "        # 准备输入给模型的提示信息\n",
    "        prompt_messages = self._prepare_find_statement_citation_prompt(\n",
    "            topic, section\n",
    "        )\n",
    "        response = await self.llm.completion(prompt_messages)\n",
    "        response = json_repair.loads(response)\n",
    "        return response[\"statements\"]\n",
    "\n",
    "    def _prepare_find_statement_citation_prompt(\n",
    "        self, topic: str, section: str\n",
    "    ):\n",
    "        \"\"\"\n",
    "        准备生成查找语句引用的模型提示消息\n",
    "\n",
    "        Args:\n",
    "            topic: 研究的主题\n",
    "            section: 调查草稿内容\n",
    "\n",
    "        Returns:\n",
    "            一个列表，包含系统和用户的提示信息\n",
    "        \"\"\"\n",
    "        system_prompt = self.find_statement_citation_prompt_template.render(\n",
    "            role=\"system\",\n",
    "            new_sections=section,\n",
    "            topic=topic\n",
    "        )\n",
    "        user_prompt = self.find_statement_citation_prompt_template.render(\n",
    "            role=\"user\",\n",
    "        )\n",
    "        return [\n",
    "            {\"role\": \"system\", \"content\": system_prompt},\n",
    "            {\"role\": \"user\", \"content\": user_prompt},\n",
    "        ]\n",
    "def sentence_tokenize(text):\n",
    "    sentences = sent_tokenize(text)\n",
    "    new_sections = \"\"\n",
    "    for sen_id,sentence in enumerate(sentences):\n",
    "        sentence = sentence.replace(\"\\n\",\".\")\n",
    "        new_sections += f\"sen_id:{sen_id}\\nsentence_text:{sentence}\\n\"\n",
    "    return new_sections\n",
    "find_statementer = FindStatementCitation()\n",
    "# 添加并发控制\n",
    "from asyncio import Semaphore\n",
    "import asyncio\n",
    "\n",
    "# 设置最大并发数\n",
    "MAX_CONCURRENT = 7\n",
    "semaphore = Semaphore(MAX_CONCURRENT)\n",
    "\n",
    "async def process_section(section, find_statementer, topic):\n",
    "    \"\"\"处理单个section的异步函数\"\"\"\n",
    "    async with semaphore:  # 使用信号量控制并发\n",
    "        try:\n",
    "            new_section = sentence_tokenize(section)\n",
    "            find_statements_result = await find_statementer.find_statement_citation(topic, new_section)\n",
    "            return find_statements_result\n",
    "        except Exception as e:\n",
    "            print(f\"处理section时出错: {str(e)}\")\n",
    "            return None\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "async def process_all_sections(sections, find_statementer, topic):\n",
    "    \"\"\"带进度条的并行处理，保持输入顺序\"\"\"\n",
    "    # 创建任务列表，并记录原始顺序\n",
    "    tasks = []\n",
    "    tasks = [process_section(section, find_statementer, topic) for section in sections]\n",
    "\n",
    "    results = await asyncio.gather(*tasks, return_exceptions=True)\n",
    "    # 过滤掉None值并返回\n",
    "    return [r for r in results if r is not None]\n",
    "# 使用方式\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_sections = [s for s in sections if len(s) > 100]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"\\n## 3.1 Benchmarks\\n\\nThe evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al. ,2018 ), SPIDER ( Yu et al. ,2018 ), KaggleDBQA ( Lee et al. ,2021 ), SEDE ( Hazoom et al. ,2021 ), and EHRSQL ( Lee et al. ,2022 ). These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets. Dr. SPIDER ( Chang et al. ,2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER. Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges. For instance, WikiSQL ( Zhong et al. ,2018 )and SPIDER ( Yu et al. ,2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al. ,2021 ), SEDE ( Hazoom et al. ,2021 ), and EHRSQL ( Lee et al. ,2022 ) aim to capture real-world scenarios. Dr. SPIDER ( Chang et al. ,2023 ) tests the robustness of existing models by perturbing either the text or schema. The AmbiQT benchmark ( Wang et al. ,2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion. Additionally, Text2Analysis ( He et al. ,2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis. These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities. \\n\\nOne of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database. WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models. However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios. For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35% . Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios .\\n\\nTo address this limitation, the Spider benchmark (Yu et al., 2018) was introduced. Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas. Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.\\n\\nAnother notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context. This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical. Each domain contains millions of passages and sufficient human-annotated query-passage related pairs, collected from real search engine systems within Alibaba Group. The authenticity of the samples allows SParC to meet the needs of both academia and industry fields, pushing forward the quality and variety of Chinese passage retrieval datasets. SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions. This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.\\n\\nAmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates. The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation. It consists of natural language questions with multiple valid SQL interpretations, each representing a different interpretation of the user\\\\'s intent. AmbiQT challenges Text2SQL models to handle ambiguity and rank multiple interpretations based on their relevance to the query. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, targeting four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity. When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution. We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling. Most outputs are small lexical tweaks of the top choice, bringing about little meaningful diversity in SQL structures or schema alternatives. Even SOTA LLMs like ChatGPT suffer from this issue.\\n\\nWikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions. WikiSQL ( Zhong et al. ,2018 ) and SPIDER ( Yu et al. ,2018 ) are popular benchmarks for the Textto-SQL task, while AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives. AmbiQT is constructed so that each text query has two distinct valid SQL interpretations, encompassing four types of ambiguity: Column Ambiguity (C), Table Ambiguity (T), Join Ambiguity (J), and Precomputed Aggregates (P). This benchmark tests performance under ambiguity in the context of current models, addressing the lack of evaluation of Text-to-SQL models under ambiguity in contemporary literature.\\n\\nThese benchmarks continue to play a crucial role in driving advancements in Text2SQL technology and ensuring the development of accurate and robust natural language interfaces to databases.\\n\\n### 3.2 Models\\n\\nThe landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning. This subsection delves into the key models that have shaped the current state of Text2SQL technology.\\n\\n**Sequence-to-Sequence Models:**\\n\\nOne of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model. This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries. The encoder processes the input question and encodes it into a fixed-length vector, while the decoder decodes this vector into the target SQL query. Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a). These models demonstrated the effectiveness of neural networks in capturing the intricacies of natural language and database schemas, laying the foundation for subsequent advancements.\\n\\n**Graph-Based Models:**\\n\\nGraph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas. These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation. Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema. These models have shown promising results in handling complex queries and improving the accuracy of generated SQL queries.\\n\\n**Hybrid Models:**\\n\\nHybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths. These models often employ a sequence-to-sequence architecture for the overall query generation process while incorporating graph-based components to handle schema reasoning and complex relationships. An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.\\n\\n**Large Language Models (LLMs):**\\n\\nThe integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness. Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy. Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.\\n\\n**Interactive Systems:**\\n\\nInteractive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries. These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings. Examples of interactive systems include the one proposed by Li et al. (2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.\\n\\nIn conclusion, the current landscape of Text2SQL models is diverse and evolving, with various architectures and approaches being explored to tackle the challenges of natural language understanding and database schema reasoning. Sequence-to-sequence models, graph-based models, hybrid models, LLMs, and interactive systems each contribute to the advancement of Text2SQL technology, paving the way for more accurate and user-friendly natural language interfaces to databases.\"]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[new_sections[7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al., 2018 ), SPIDER ( Yu et al., 2018 ), KaggleDBQA ( Lee et al., 2021 ), SEDE ( Hazoom et al., 2021 ), and EHRSQL ( Lee et al., 2022 ).\n",
      "[0, 1, 2, 3, 4, 5]\n",
      "\n",
      "## 3.1 Benchmarks\n",
      "\n",
      "The evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al.\n",
      ",2018 ), SPIDER ( Yu et al.\n",
      ",2018 ), KaggleDBQA ( Lee et al.\n",
      ",2021 ), SEDE ( Hazoom et al.\n",
      ",2021 ), and EHRSQL ( Lee et al.\n",
      ",2022 ).\n",
      "These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets.\n",
      "[6]\n",
      "These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets.\n",
      "Dr. SPIDER ( Chang et al., 2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER.\n",
      "[7, 8]\n",
      "Dr. SPIDER ( Chang et al.\n",
      ",2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER.\n",
      "Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges.\n",
      "[9]\n",
      "Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges.\n",
      "For instance, WikiSQL ( Zhong et al., 2018 ) and SPIDER ( Yu et al., 2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al., 2021 ), SEDE ( Hazoom et al., 2021 ), and EHRSQL ( Lee et al., 2022 ) aim to capture real-world scenarios.\n",
      "[10, 11, 12, 13, 14, 15]\n",
      "For instance, WikiSQL ( Zhong et al.\n",
      ",2018 )and SPIDER ( Yu et al.\n",
      ",2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al.\n",
      ",2021 ), SEDE ( Hazoom et al.\n",
      ",2021 ), and EHRSQL ( Lee et al.\n",
      ",2022 ) aim to capture real-world scenarios.\n",
      "The AmbiQT benchmark ( Wang et al., 2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.\n",
      "[18, 19]\n",
      "The AmbiQT benchmark ( Wang et al.\n",
      ",2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.\n",
      "Additionally, Text2Analysis ( He et al., 2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.\n",
      "[20, 21]\n",
      "Additionally, Text2Analysis ( He et al.\n",
      ",2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.\n",
      "These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.\n",
      "[22]\n",
      "These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.\n",
      "One of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database.\n",
      "[23]\n",
      "One of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database.\n",
      "WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models.\n",
      "[24]\n",
      "WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models.\n",
      "However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios.\n",
      "[25]\n",
      "However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios.\n",
      "For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35%!\n",
      "[26]\n",
      "For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35% .\n",
      "Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios.\n",
      "[27]\n",
      "Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios .\n",
      "To address this limitation, the Spider benchmark (Yu et al., 2018) was introduced.\n",
      "[28]\n",
      "To address this limitation, the Spider benchmark (Yu et al., 2018) was introduced.\n",
      "Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas.\n",
      "[29]\n",
      "Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas.\n",
      "Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.\n",
      "[30]\n",
      "Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.\n",
      "Another notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context.\n",
      "[31]\n",
      "Another notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context.\n",
      "This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical.\n",
      "[32, 33, 34]\n",
      "This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical.\n",
      "Each domain contains millions of passages and sufficient human-annotated query-passage related pairs, collected from real search engine systems within Alibaba Group.\n",
      "The authenticity of the samples allows SParC to meet the needs of both academia and industry fields, pushing forward the quality and variety of Chinese passage retrieval datasets.\n",
      "SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions.\n",
      "[35]\n",
      "SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions.\n",
      "This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.\n",
      "[36]\n",
      "This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.\n",
      "AmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL.\n",
      "[37]\n",
      "AmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL.\n",
      "AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.\n",
      "[38, 39, 40, 41, 42]\n",
      "AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.\n",
      "The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation.\n",
      "It consists of natural language questions with multiple valid SQL interpretations, each representing a different interpretation of the user\\'s intent.\n",
      "AmbiQT challenges Text2SQL models to handle ambiguity and rank multiple interpretations based on their relevance to the query.\n",
      "AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, targeting four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity.\n",
      "When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution.\n",
      "[43]\n",
      "When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution.\n",
      "We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling.\n",
      "[44, 45, 46]\n",
      "We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling.\n",
      "Most outputs are small lexical tweaks of the top choice, bringing about little meaningful diversity in SQL structures or schema alternatives.\n",
      "Even SOTA LLMs like ChatGPT suffer from this issue.\n",
      "WikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions.\n",
      "[47]\n",
      "WikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions.\n",
      "The landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning.\n",
      "[54]\n",
      "### 3.2 Models\n",
      "\n",
      "The landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning.\n",
      "One of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model.\n",
      "[56]\n",
      "**Sequence-to-Sequence Models:**\n",
      "\n",
      "One of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model.\n",
      "This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries.\n",
      "[57]\n",
      "This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries.\n",
      "Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a).\n",
      "[59]\n",
      "Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a).\n",
      "Graph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas.\n",
      "[61]\n",
      "**Graph-Based Models:**\n",
      "\n",
      "Graph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas.\n",
      "These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation.\n",
      "[62]\n",
      "These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation.\n",
      "Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema.\n",
      "[63]\n",
      "Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema.\n",
      "Hybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths.\n",
      "[65]\n",
      "**Hybrid Models:**\n",
      "\n",
      "Hybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths.\n",
      "An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.\n",
      "[67]\n",
      "An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.\n",
      "The integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness.\n",
      "[68]\n",
      "**Large Language Models (LLMs):**\n",
      "\n",
      "The integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness.\n",
      "Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy.\n",
      "[69]\n",
      "Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy.\n",
      "Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.\n",
      "[70]\n",
      "Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.\n",
      "Interactive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries.\n",
      "[71]\n",
      "**Interactive Systems:**\n",
      "\n",
      "Interactive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries.\n",
      "These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings.\n",
      "[72]\n",
      "These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings.\n",
      "Examples of interactive systems include the one proposed by Li et al. (2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.\n",
      "[73, 74]\n",
      "Examples of interactive systems include the one proposed by Li et al.\n",
      "(2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.\n"
     ]
    }
   ],
   "source": [
    "for r in results[0]:\n",
    "    print(r[\"statement\"])\n",
    "    print(r[\"related_sen_id\"])\n",
    "    # a = re.findall(r\"{}\".format(r[\"statement\"]),new_section)\n",
    "    sentences = sent_tokenize(new_sections[7])\n",
    "    for id in r[\"related_sen_id\"]:\n",
    "        print(sentences[id])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'statement': 'The evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al., 2018 ), SPIDER ( Yu et al., 2018 ), KaggleDBQA ( Lee et al., 2021 ), SEDE ( Hazoom et al., 2021 ), and EHRSQL ( Lee et al., 2022 ).',\n",
       "   'related_sen_id': [0, 1, 2, 3, 4, 5],\n",
       "   'statement_hyde': 'The assessment of Text2SQL models is critically dependent on the presence of extensive and varied benchmarks, including WikiSQL (developed by Zhong et al., 2018), SPIDER (introduced by Yu et al., 2018), KaggleDBQA (crafted by Lee et al., 2021), SEDE (created by Hazoom et al., 2021), and EHRSQL (designed by Lee et al., 2022).'},\n",
       "  {'statement': 'These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'Such benchmarks are instrumental in evaluating both the performance and robustness of Text2SQL models, and they also tackle the issue of ambiguity prevalent in real-world datasets.'},\n",
       "  {'statement': 'Dr. SPIDER ( Chang et al., 2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER.',\n",
       "   'related_sen_id': [7, 8],\n",
       "   'statement_hyde': 'Dr. SPIDER, introduced by Chang et al. (2023), serves as an additional benchmark aimed at evaluating the robustness of existing Text2SQL models through the perturbation of either the textual input or the schema of the SPIDER benchmark.'},\n",
       "  {'statement': 'Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges.',\n",
       "   'related_sen_id': [9],\n",
       "   'statement_hyde': 'A variety of notable benchmarks have arisen within the Text2SQL domain, each characterized by distinct features and inherent challenges.'},\n",
       "  {'statement': 'For instance, WikiSQL ( Zhong et al., 2018 ) and SPIDER ( Yu et al., 2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al., 2021 ), SEDE ( Hazoom et al., 2021 ), and EHRSQL ( Lee et al., 2022 ) aim to capture real-world scenarios.',\n",
       "   'related_sen_id': [10, 11, 12, 13, 14, 15],\n",
       "   'statement_hyde': 'For example, WikiSQL (developed by Zhong et al., 2018) and SPIDER (introduced by Yu et al., 2018) are widely-used benchmarks focusing on fundamental tasks, whereas benchmarks such as KaggleDBQA (crafted by Lee et al., 2021), SEDE (created by Hazoom et al., 2021), and EHRSQL (designed by Lee et al., 2022) are designed to reflect real-world scenarios.'},\n",
       "  {'statement': 'The AmbiQT benchmark ( Wang et al., 2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.',\n",
       "   'related_sen_id': [18, 19],\n",
       "   'statement_hyde': 'The AmbiQT benchmark, introduced by Wang et al. (2022), stands as the inaugural open benchmark specifically designed to test the coverage of ambiguous alternatives in the Text-to-SQL conversion process.'},\n",
       "  {'statement': 'Additionally, Text2Analysis ( He et al., 2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.',\n",
       "   'related_sen_id': [20, 21],\n",
       "   'statement_hyde': 'Furthermore, Text2Analysis, developed by He et al. (2023), addresses the existing research gap related to advanced analysis tasks and the handling of ambiguous queries within the realm of tabular data analysis.'},\n",
       "  {'statement': 'These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.',\n",
       "   'related_sen_id': [22],\n",
       "   'statement_hyde': 'Collectively, these benchmarks pose significant challenges in the field of tabular data analysis, thereby opening avenues for more sophisticated research endeavors.'},\n",
       "  {'statement': 'One of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database.',\n",
       "   'related_sen_id': [23],\n",
       "   'statement_hyde': 'Among the most extensively utilized benchmarks is WikiSQL, developed by Zhong et al. (2017), comprising natural language questions paired with their corresponding SQL queries within a single database context.'},\n",
       "  {'statement': 'WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models.',\n",
       "   'related_sen_id': [24],\n",
       "   'statement_hyde': 'WikiSQL emphasizes simple and direct queries, rendering it appropriate for assessing the fundamental capabilities of Text2SQL models.'},\n",
       "  {'statement': 'However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios.',\n",
       "   'related_sen_id': [25],\n",
       "   'statement_hyde': 'Nevertheless, the restricted scope and absence of complex queries in WikiSQL may fail to adequately mirror the challenges prevalent in real-world applications.'},\n",
       "  {'statement': 'For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35%!',\n",
       "   'related_sen_id': [26],\n",
       "   'statement_hyde': 'For instance, the KITAB dataset, centered on literature-related queries, illustrates that even cutting-edge LLMs such as GPT4 and GPT3.5 face difficulties in constraint satisfaction, frequently generating irrelevant information, with the rate of fully correct responses significantly below 35%.'},\n",
       "  {'statement': 'Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios.',\n",
       "   'related_sen_id': [27],\n",
       "   'statement_hyde': 'Moreover, the assessment of LLMs on complex queries involving multiple constraint types and extended outputs remains a substantial challenge, given that numerous existing benchmarks have reached saturation and fail to offer a thorough evaluation of LLM performance in such contexts.'},\n",
       "  {'statement': 'To address this limitation, the Spider benchmark (Yu et al., 2018) was introduced.',\n",
       "   'related_sen_id': [28],\n",
       "   'statement_hyde': 'To mitigate this limitation, the Spider benchmark, introduced by Yu et al. (2018), was developed.'},\n",
       "  {'statement': 'Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas.',\n",
       "   'related_sen_id': [29],\n",
       "   'statement_hyde': 'Spider includes a varied array of complex and cross-domain questions, covering multiple databases characterized by diverse schemas.'},\n",
       "  {'statement': 'Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.',\n",
       "   'related_sen_id': [30],\n",
       "   'statement_hyde': 'Spider has emerged as a preferred benchmark for assessing the generalization abilities of Text2SQL models.'},\n",
       "  {'statement': 'Another notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context.',\n",
       "   'related_sen_id': [31],\n",
       "   'statement_hyde': 'Another significant benchmark is SParC, introduced by Yu et al. (2019), which concentrates on cross-domain semantic parsing within a contextual framework.'},\n",
       "  {'statement': 'This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical.',\n",
       "   'related_sen_id': [32, 33, 34],\n",
       "   'statement_hyde': 'SParC addresses the shortcomings of earlier benchmarks by offering a large-scale dataset that encompasses multiple specific domains for Chinese passage retrieval, such as E-commerce, Entertainment video, and Medical, thereby enhancing the diversity and applicability of the dataset.'},\n",
       "  {'statement': 'SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions.',\n",
       "   'related_sen_id': [35],\n",
       "   'statement_hyde': 'SParC introduces a more realistic evaluation environment through the inclusion of multi-turn dialogues and context-dependent questions.'},\n",
       "  {'statement': 'This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.',\n",
       "   'related_sen_id': [36],\n",
       "   'statement_hyde': 'This benchmark assesses the capability of Text2SQL models to retain context and produce accurate queries based on preceding interactions.'},\n",
       "  {'statement': 'AmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL.',\n",
       "   'related_sen_id': [37],\n",
       "   'statement_hyde': 'AmbiQT, introduced by Wang et al. (2022), is a recent benchmark specifically designed to address the issue of ambiguity within Text2SQL tasks.'},\n",
       "  {'statement': 'AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.',\n",
       "   'related_sen_id': [38, 39, 40, 41, 42],\n",
       "   'statement_hyde': 'AmbiQT comprises over 3000 examples, each linking a natural language question to a database with two valid SQL interpretations, and covers four distinct types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.'},\n",
       "  {'statement': 'When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution.',\n",
       "   'related_sen_id': [43],\n",
       "   'statement_hyde': 'In the presence of ambiguity, an optimal Text-toSQL system should include all valid alternatives within their top $k$ SQL outputs, allowing for user resolution.'},\n",
       "  {'statement': 'We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling.',\n",
       "   'related_sen_id': [44, 45, 46],\n",
       "   'statement_hyde': 'Our analysis reveals that current approaches, spanning from T5-3B to state-of-the-art models, are unable to generate all ambiguous outputs regardless of the decoding strategy employed, including beam search and diversity-enhancing sampling methods like Nucleus and Typical sampling.'},\n",
       "  {'statement': 'WikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions.',\n",
       "   'related_sen_id': [47],\n",
       "   'statement_hyde': 'WikiSQL, Spider, SParC, and AmbiQT each play a role in evaluating various facets of Text2SQL models, ranging from fundamental query generation to the management of ambiguity and context-dependent queries.'},\n",
       "  {'statement': 'The landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning.',\n",
       "   'related_sen_id': [54],\n",
       "   'statement_hyde': 'The Text2SQL model landscape has undergone significant evolution, with a multitude of architectures and methodologies being investigated to address the complexities inherent in natural language understanding and database schema reasoning.'},\n",
       "  {'statement': 'One of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model.',\n",
       "   'related_sen_id': [56],\n",
       "   'statement_hyde': 'Among the earliest and most impactful approaches to Text2SQL is the sequence-to-sequence model.'},\n",
       "  {'statement': 'This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries.',\n",
       "   'related_sen_id': [57],\n",
       "   'statement_hyde': 'This architecture, drawing inspiration from neural machine translation, utilizes an encoder-decoder framework to convert natural language questions into SQL queries.'},\n",
       "  {'statement': 'Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a).',\n",
       "   'related_sen_id': [59],\n",
       "   'statement_hyde': 'Prominent examples of sequence-to-sequence models within the Text2SQL domain include Seq2SQL, developed by Zhong et al. (2017), and RATSQL, introduced by Wang et al. (2020a).'},\n",
       "  {'statement': 'Graph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas.',\n",
       "   'related_sen_id': [61],\n",
       "   'statement_hyde': 'Graph-based models have garnered attention in the Text2SQL field owing to their capability to represent the structured characteristics of database schemas.'},\n",
       "  {'statement': 'These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation.',\n",
       "   'related_sen_id': [62],\n",
       "   'statement_hyde': 'These models leverage graph structures to encode the relationships among tables, columns, and cell values, facilitating more effective reasoning and query generation processes.'},\n",
       "  {'statement': 'Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema.',\n",
       "   'related_sen_id': [63],\n",
       "   'statement_hyde': 'Illustrative examples of graph-based models are GraphSQL, developed by Yao et al. (2019), and Graphix-T5, introduced by Li et al. (2023b), both of which employ graph neural networks to capture the dependencies and relationships within the database schema.'},\n",
       "  {'statement': 'Hybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths.',\n",
       "   'related_sen_id': [65],\n",
       "   'statement_hyde': 'Hybrid models integrate components from both sequence-to-sequence and graph-based models to harness their respective strengths.'},\n",
       "  {'statement': 'An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.',\n",
       "   'related_sen_id': [67],\n",
       "   'statement_hyde': 'A representative hybrid model is RESDSQL, developed by Li et al. (2023a), which decouples schema linking and skeleton parsing to enhance the accuracy and efficiency of Text2SQL systems.'},\n",
       "  {'statement': 'The integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness.',\n",
       "   'related_sen_id': [68],\n",
       "   'statement_hyde': 'The incorporation of Large Language Models (LLMs) into Text2SQL has transformative impacts on the field, providing unparalleled levels of language understanding and context-awareness.'},\n",
       "  {'statement': 'Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy.',\n",
       "   'related_sen_id': [69],\n",
       "   'statement_hyde': 'The fine-tuning of pre-trained LLMs, such as BERT (developed by Devlin et al., 2019) and GPT (introduced by Radford et al., 2018), on Text2SQL tasks has resulted in substantial enhancements in query generation accuracy.'},\n",
       "  {'statement': 'Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.',\n",
       "   'related_sen_id': [70],\n",
       "   'statement_hyde': 'Models such as Grappa, developed by Yu et al. (2020), and T5, introduced by Raffel et al. (2020), exemplify the potential of LLMs in capturing the subtleties of natural language and producing accurate SQL queries.'},\n",
       "  {'statement': 'Interactive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries.',\n",
       "   'related_sen_id': [71],\n",
       "   'statement_hyde': 'Interactive Text2SQL systems are pivotal in addressing the inherent ambiguity present in natural language queries.'},\n",
       "  {'statement': 'These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings.',\n",
       "   'related_sen_id': [72],\n",
       "   'statement_hyde': 'These systems involve users in a step-by-step dialogue process to elucidate their intent and resolve potential ambiguities.'},\n",
       "  {'statement': 'Examples of interactive systems include the one proposed by Li et al. (2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.',\n",
       "   'related_sen_id': [73, 74],\n",
       "   'statement_hyde': 'Notable examples of interactive systems include the approach proposed by Li et al. (2020), which utilizes a parser-independent interactive method to enhance query comprehension and boost the accuracy of the generated SQL queries.'}]]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = await process_all_sections([new_sections[7]], find_statementer, topic)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "deepseek-r1\n",
    "```json\n",
    "{\n",
    "\"statements\": [\n",
    "    {\n",
    "        \"statement\": \"The evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL (Zhong et al., 2018), SPIDER (Yu et al., 2018), KaggleDBQA (Lee et al., 2021), SEDE (Hazoom et al., 2021), and EHRSQL (Lee et al., 2022). These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets.\",\n",
    "        \"related_sen_id\": [1, 2],\n",
    "        \"statement_hyde\": \"Recent surveys of Text2SQL evaluation frameworks (Zhong et al., 2018; Yu et al., 2018) demonstrate that domain-specific benchmarks like WikiSQL and SPIDER have become foundational for model comparison. Subsequent studies (Lee et al., 2021; Hazoom et al., 2021) expanded this landscape with real-world scenario datasets, while analysis by Lee et al. (2022) in healthcare domains revealed persistent challenges in schema alignment. The collective evolution of these benchmarks reflects an academic consensus that multi-dimensional evaluation must address both structural complexity and semantic ambiguity.\"\n",
    "    },\n",
    "    {\n",
    "        \"statement\": \"Dr. SPIDER (Chang et al., 2023) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER. Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges.\",\n",
    "        \"related_sen_id\": [3, 4],\n",
    "        \"statement_hyde\": \"Methodological innovations in benchmark design are exemplified by Dr. SPIDER (Chang et al., 2023), which introduced controlled schema perturbations to stress-test model robustness. Comparative analyses across SPIDER derivatives (Yu et al., 2018; Chang et al., 2023) reveal significant performance variations under schema modifications, highlighting the need for adaptive schema linking mechanisms in modern Text2SQL architectures.\"\n",
    "    },\n",
    "    {\n",
    "        \"statement\": \"The AmbiQT benchmark (Wang et al., 2022) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion. Additionally, Text2Analysis (He et al., 2023) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.\",\n",
    "        \"related_sen_id\": [5, 6],\n",
    "        \"statement_hyde\": \"Semantic ambiguity quantification reached new heights with AmbiQT's introduction (Wang et al., 2022), establishing systematic metrics for alternative SQL interpretation coverage. Parallel developments like Text2Analysis (He et al., 2023) extended evaluation to analytical query complexity, creating testbeds for higher-order reasoning capabilities beyond basic SELECT-WHERE patterns.\"\n",
    "    },\n",
    "    {\n",
    "        \"statement\": \"WikiSQL (Zhong et al., 2017) consists of natural language questions paired with corresponding SQL queries on a single database. However, its limited scope and lack of complex queries may not adequately reflect real-world challenges. The KITAB dataset demonstrates that state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction, with full correctness remaining notably lower than 35%.\",\n",
    "        \"related_sen_id\": [7, 8, 9],\n",
    "        \"statement_hyde\": \"While WikiSQL (Zhong et al., 2017) established baseline performance metrics, subsequent analyses using domain-specific datasets like KITAB revealed fundamental limitations in LLM-based approaches. Empirical studies demonstrate that even advanced models (OpenAI, 2022) achieve <35% full correctness on constraint-rich queries, underscoring persistent challenges in semantic alignment and logical constraint satisfaction.\"\n",
    "    },\n",
    "    {\n",
    "        \"statement\": \"The Spider benchmark (Yu et al., 2018) encompasses cross-domain questions spanning multiple databases. SParC (Yu et al., 2019) provides multi-domain Chinese passage retrieval data from Alibaba systems, while AmbiQT (Wang et al., 2022) tests ambiguity handling through multiple valid SQL interpretations.\",\n",
    "        \"related_sen_id\": [10, 11, 12],\n",
    "        \"statement_hyde\": \"Cross-domain evaluation evolved through Spider's multi-database framework (Yu et al., 2018) and SParC's industry-aligned Chinese dataset (Yu et al., 2019), with AmbiQT (Wang et al., 2022) introducing structural ambiguity metrics. Comparative studies show performance gaps between single-domain (WikiSQL) and cross-domain (Spider) scenarios exceeding 22% accuracy, emphasizing the complexity of schema generalization.\"\n",
    "    },\n",
    "    {\n",
    "        \"statement\": \"Sequence-to-sequence models like Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a) demonstrated neural networks' effectiveness in Text2SQL. Graph-based models (Yao et al., 2019; Li et al., 2023b) improved schema reasoning through graph neural networks.\",\n",
    "        \"related_sen_id\": [13, 14],\n",
    "        \"statement_hyde\": \"Architectural progression from early seq2seq models (Zhong et al., 2017) to graph-enhanced architectures (Yao et al., 2019) reflects increasing attention to structural reasoning. Benchmarks reveal that graph-based approaches (Li et al., 2023b) achieve 15-20% higher accuracy on multi-table joins compared to pure sequence models, validating the importance of explicit schema representation.\"\n",
    "    },\n",
    "    {\n",
    "        \"statement\": \"Hybrid models like RESDSQL (Li et al., 2023a) combine sequence and graph components, while LLM fine-tuning (Devlin et al., 2019; Raffel et al., 2020) advanced language understanding. Interactive systems (Li et al., 2020) address ambiguity through step-by-step clarification.\",\n",
    "        \"related_sen_id\": [15, 16, 17],\n",
    "        \"statement_hyde\": \"Modern hybrid architectures (Li et al., 2023a) blend neural translation paradigms with graph-based schema linking, achieving 89% execution accuracy on Spider. The LLM revolution (Raffel et al., 2020) brought transformer-based semantic parsing, though studies show interactive systems (Li et al., 2020) remain crucial for resolving 68% of ambiguous queries through dialog.\"\n",
    "    }\n",
    "]\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'statement': 'Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'Traditional research in the domain of Text-to-SQL generation has predominantly concentrated on scenarios wherein a single natural language query corresponds to a unique, correct SQL query, as documented in [Reference].'},\n",
       "  {'statement': 'However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'Contrary to idealized scenarios, real-world databases frequently present substantial ambiguity in natural language queries, stemming from overlapping schema names, multiple relationship paths, and various other contributing factors, as highlighted in [Reference].'},\n",
       "  {'statement': 'This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.',\n",
       "   'related_sen_id': [4],\n",
       "   'statement_hyde': 'Such ambiguity can result in the existence of multiple SQL queries that yield correct answers, despite the fact that the majority of existing benchmarks only furnish a single query from the numerous potential correct queries, as noted in [Reference].'},\n",
       "  {'statement': \"This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.\",\n",
       "   'related_sen_id': [5],\n",
       "   'statement_hyde': \"The aforementioned ambiguity presents a significant challenge to current Text-to-SQL systems, which often encounter difficulties in generating both accurate and diverse SQL queries that encompass all potential interpretations of the user's intent, as discussed in [Reference].\"},\n",
       "  {'statement': 'To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'In response to this identified gap, we have developed a novel benchmark, termed AmbiQT, comprising over 3000 examples wherein each natural language query can be interpreted as two plausible SQL queries owing to lexical and/or structural ambiguity, as detailed in [Reference].'},\n",
       "  {'statement': 'Benchmarks like PredBench have limitations in terms of training, benchmarking, and evaluation.',\n",
       "   'related_sen_id': [10],\n",
       "   'statement_hyde': 'Benchmarks such as PredBench have been observed to possess limitations in the realms of training, benchmarking, and evaluation processes, as critically analyzed in [Reference].'},\n",
       "  {'statement': 'For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.',\n",
       "   'related_sen_id': [11],\n",
       "   'statement_hyde': 'For example, training limitations are characterized by constraints on model architecture and size, whereas benchmark limitations are evident in the restricted number of methods and the necessity for additional calibration of dataset protocols, as elaborated in [Reference].'},\n",
       "  {'statement': 'Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.',\n",
       "   'related_sen_id': [12],\n",
       "   'statement_hyde': 'Evaluation limitations are manifested in the utilization of a small and homogenous sample of human evaluators, coupled with the absence of diverse evaluation approaches and metrics, as pointed out in [Reference].'},\n",
       "  {'statement': 'To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.',\n",
       "   'related_sen_id': [13],\n",
       "   'statement_hyde': 'To mitigate these limitations, future research endeavors could delve into the exploration of additional evaluation methods, enhance the diversity and size of participant pools, and scrutinize the influence of diverse hyperparameters on model performance, as proposed in [Reference].'},\n",
       "  {'statement': 'Additionally, incorporating indicators of attack failures could help in debugging faulty evaluations and lead to fairer assessments.',\n",
       "   'related_sen_id': [14],\n",
       "   'statement_hyde': 'Furthermore, the integration of indicators of attack failures has the potential to aid in the debugging of faulty evaluations, thereby contributing to more equitable assessments, as suggested in [Reference].'},\n",
       "  {'statement': \"Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments to evaluate models' ability to exhibit rational behavior in economic tasks.\",\n",
       "   'related_sen_id': [15],\n",
       "   'statement_hyde': 'Moreover, the incorporation of economic rationality assessments into benchmarks could be advantageous for evaluating the capacity of models to demonstrate rational behavior in the context of economic tasks, as recommended in [Reference].'},\n",
       "  {'statement': 'The AmbiQT benchmark addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.',\n",
       "   'related_sen_id': [17],\n",
       "   'statement_hyde': 'The AmbiQT benchmark addresses SQL ambiguity by encompassing four distinct types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates, as described in [Reference].'},\n",
       "  {'statement': 'The work on interactive Text-to-SQL generation proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.',\n",
       "   'related_sen_id': [18],\n",
       "   'statement_hyde': 'Research on interactive Text-to-SQL generation introduces a novel interaction mechanism enabling users to validate and refine generated queries via step-by-step explanations, a method that can be extended to support multi-turn SQL generation by integrating the contextual information from prior queries into both explanation generation and text-to-clause generation processes, as outlined in [Reference].'},\n",
       "  {'statement': \"The exploration of chain-of-thought style prompting for Text-to-SQL aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.\",\n",
       "   'related_sen_id': [19],\n",
       "   'statement_hyde': 'The investigation into chain-of-thought style prompting for Text-to-SQL seeks to augment the reasoning capabilities of large language models by systematically examining CoT style prompting for text-to-SQL parsing, thereby addressing the intricate, multistep reasoning demands of the task, as detailed in [Reference].'},\n",
       "  {'statement': 'We also address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.',\n",
       "   'related_sen_id': [20],\n",
       "   'statement_hyde': 'Additionally, we tackle the ethical considerations pertinent to Text2SQL technology, especially within sensitive domains, and deliberate on strategies aimed at mitigating bias and ensuring fairness, as discussed in [Reference].'},\n",
       "  {'statement': 'Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.',\n",
       "   'related_sen_id': [21],\n",
       "   'statement_hyde': 'In conclusion, we pinpoint promising avenues for future research aimed at advancing Text2SQL technology, thereby unlocking its comprehensive potential to empower users in accessing and analyzing data more effectively, as proposed in [Reference].'}],\n",
       " [{'statement': 'The early approaches to Text2SQL can be categorized into rule-based systems and grammar-based methods.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'Early methodologies in Text2SQL have been predominantly classified into two primary categories: rule-based systems and grammar-based approaches, as documented in the foundational literature.'},\n",
       "  {'statement': 'Rule-based systems, such as the one proposed by Hendrix et al. (1978), relied on handcrafted rules to map natural language questions to SQL queries.',\n",
       "   'related_sen_id': [1, 2],\n",
       "   'statement_hyde': 'Hendrix et al. (1978) introduced a pioneering rule-based system that utilized manually crafted rules for the translation of natural language queries into SQL, a method that has been extensively referenced in subsequent research.'},\n",
       "  {'statement': 'These systems were limited in their ability to handle complex queries and required extensive manual effort to create and maintain the rules.',\n",
       "   'related_sen_id': [9],\n",
       "   'statement_hyde': 'It has been widely acknowledged that rule-based systems exhibit significant limitations in managing complex queries, necessitating considerable manual intervention for rule formulation and maintenance, as highlighted in various empirical studies.'},\n",
       "  {'statement': 'Grammar-based methods, like the one developed by Giordani and Moschitti (2012), used generative parsers to translate questions into SQL queries.',\n",
       "   'related_sen_id': [10],\n",
       "   'statement_hyde': 'Giordani and Moschitti (2012) developed a grammar-based method that employed generative parsers for the conversion of questions into SQL queries, a technique that has been critically analyzed in the field.'},\n",
       "  {'statement': 'While these methods offered some flexibility, they still struggled with the inherent complexity and ambiguity of natural language.',\n",
       "   'related_sen_id': [11],\n",
       "   'statement_hyde': 'Despite providing a degree of flexibility, grammar-based methods have consistently faced challenges in addressing the intrinsic complexity and ambiguity inherent in natural language, as evidenced by multiple comparative studies.'},\n",
       "  {'statement': 'For instance, the approach necessitates meticulous prompt design to generate exercises, which inevitably entails human intervention, introducing potential bias or variability and may not scale efficiently.',\n",
       "   'related_sen_id': [12],\n",
       "   'statement_hyde': 'The necessity for meticulous prompt design in these approaches inherently requires human intervention, thereby introducing potential biases and variabilities, and posing scalability challenges, as discussed in recent methodological reviews.'},\n",
       "  {'statement': 'Additionally, the quality and correctness of the generated problems are not explicitly addressed, and the current framework relies on a source problem for exercise generation, limiting flexibility and robustness.',\n",
       "   'related_sen_id': [13],\n",
       "   'statement_hyde': \"The literature indicates that the quality and correctness of generated problems remain unaddressed, with the existing framework's dependency on a source problem for exercise generation compromising its flexibility and robustness.\"},\n",
       "  {'statement': 'Furthermore, the handling of ambiguity in natural language is a significant challenge, as models often fail to capture the distribution of possible meanings without deliberate instruction.',\n",
       "   'related_sen_id': [14],\n",
       "   'statement_hyde': 'The significant challenge of handling natural language ambiguity is well-documented, with models frequently failing to accurately capture the distribution of potential meanings in the absence of explicit guidance, as noted in several recent studies.'}],\n",
       " [{'statement': 'The advent of deep learning brought about a paradigm shift in the field of Text2SQL, enabling the construction of several large text-to-SQL datasets, such as WikiSQL (Zhong et al., 2017) and Spider (Yu et al., 2018), and achieving unprecedented performance in recent years (Rubin and Berant, 2021; Wang et al., 2020a; Scholak et al., 2021; Yu et al., 2020; Hwang et al., 2019).',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'The emergence of deep learning has significantly altered the landscape of Text2SQL, facilitating the creation of extensive text-to-SQL datasets like WikiSQL and Spider. Recent advancements, as documented by Rubin and Berant (2021), Wang et al. (2020a), Scholak et al. (2021), Yu et al. (2020), and Hwang et al. (2019), have led to unparalleled performance improvements in this domain.'},\n",
       "  {'statement': 'Neural network-based models, particularly sequence-to-sequence models, demonstrated remarkable improvements in translation accuracy and generalization capabilities.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'It has been observed that models based on neural networks, especially sequence-to-sequence architectures, have shown substantial enhancements in both translation accuracy and generalization abilities.'},\n",
       "  {'statement': 'Notable examples include Seq2SQL (Zhong et al., 2017), which employed reinforcement learning to generate SQL queries, and RATSQL (Wang et al., 2020a), which introduced a relation-aware self-attention mechanism to better encode the relationships between columns and tables.',\n",
       "   'related_sen_id': [2],\n",
       "   'statement_hyde': 'Significant contributions include Seq2SQL, which utilized reinforcement learning for SQL query generation, and RATSQL, which introduced a relation-aware self-attention mechanism to more effectively encode inter-column and inter-table relationships, as detailed by Zhong et al. (2017) and Wang et al. (2020a), respectively.'},\n",
       "  {'statement': 'These models leveraged the power of deep learning to capture the complexities of natural language and database schemas, leading to more accurate and robust Text2SQL systems.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'By harnessing deep learning, these models have been able to encapsulate the intricacies of natural language and database schemas, thereby enhancing the accuracy and robustness of Text2SQL systems.'},\n",
       "  {'statement': 'Furthermore, the integration of large language models (LLMs) like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) into Text2SQL further pushed the boundaries of performance.',\n",
       "   'related_sen_id': [4],\n",
       "   'statement_hyde': 'Additionally, the incorporation of large language models such as BERT and GPT into Text2SQL has significantly extended performance limits, as evidenced by the works of Devlin et al. (2019) and Radford et al. (2018).'},\n",
       "  {'statement': 'These pre-trained models, fine-tuned on Text2SQL tasks, demonstrated superior understanding of language semantics and context, resulting in more accurate query generation.',\n",
       "   'related_sen_id': [5],\n",
       "   'statement_hyde': 'Pre-trained models, when fine-tuned for Text2SQL tasks, have exhibited an advanced grasp of language semantics and context, thereby improving the precision of query generation.'},\n",
       "  {'statement': 'For instance, Grappa (Yu et al., 2020) combined grammar-augmented pre-training with table semantic parsing, showcasing the potential of LLMs in Text2SQL.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'For example, Grappa, as presented by Yu et al. (2020), integrated grammar-augmented pre-training with table semantic parsing, highlighting the capabilities of LLMs in the Text2SQL domain.'},\n",
       "  {'statement': 'The deep learning era also witnessed the emergence of interactive Text2SQL systems, which aimed to address the ambiguity inherent in natural language queries.',\n",
       "   'related_sen_id': [7],\n",
       "   'statement_hyde': 'The era of deep learning has also seen the rise of interactive Text2SQL systems, designed to mitigate the ambiguities inherent in natural language queries.'},\n",
       "  {'statement': 'These systems, such as the one proposed by Li et al. (2020), employed parser-independent interactive approaches to enhance query understanding and disambiguation.',\n",
       "   'related_sen_id': [8, 9],\n",
       "   'statement_hyde': 'Systems like the one proposed by Li et al. (2020) have utilized parser-independent interactive methodologies to improve query comprehension and disambiguation.'},\n",
       "  {'statement': 'By engaging users in a step-by-step dialogue, these systems could clarify ambiguities and generate more accurate SQL queries.',\n",
       "   'related_sen_id': [10],\n",
       "   'statement_hyde': 'Through a step-by-step dialogic process with users, these systems are capable of resolving ambiguities and producing more precise SQL queries.'},\n",
       "  {'statement': 'In summary, the deep learning era marked a significant leap forward in Text2SQL technology.',\n",
       "   'related_sen_id': [11],\n",
       "   'statement_hyde': 'To summarize, the deep learning era has represented a substantial advancement in Text2SQL technology.'},\n",
       "  {'statement': 'The integration of neural networks, LLMs, and interactive systems revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.',\n",
       "   'related_sen_id': [12],\n",
       "   'statement_hyde': 'The incorporation of neural networks, large language models, and interactive systems has revolutionized the Text2SQL field, resulting in systems that are more accurate, robust, and user-friendly.'}],\n",
       " [{'statement': 'The integration of large language models (LLMs) into Text2SQL has significantly advanced the field.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'The incorporation of large language models, such as BERT and GPT, into Text2SQL systems has been shown to markedly progress the field, as evidenced by numerous studies highlighting enhanced performance metrics.'},\n",
       "  {'statement': 'LLMs, such as BERT (Devlin et al., 2019) and GPT (Radford et al., 2018), have demonstrated remarkable capabilities in understanding language semantics and context, leading to more accurate and robust query generation.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'It has been demonstrated by Devlin et al. (2019) and Radford et al. (2018) that large language models like BERT and GPT possess exceptional abilities in comprehending language semantics and context, thereby facilitating the generation of more precise and resilient SQL queries.'},\n",
       "  {'statement': 'Fine-tuning these pre-trained models on Text2SQL tasks has proven to be highly effective, as evidenced by the success of models like Grappa (Yu et al., 2020), which combines grammar-augmented pre-training with table semantic parsing.',\n",
       "   'related_sen_id': [2],\n",
       "   'statement_hyde': 'The process of fine-tuning pre-trained large language models for Text2SQL tasks has been validated as highly effective, exemplified by the achievements of models such as Grappa (Yu et al., 2020), which integrates grammar-augmented pre-training techniques with advanced table semantic parsing methodologies.'},\n",
       "  {'statement': 'The use of LLMs has also enabled the development of more user-friendly and interactive Text2SQL systems, which can better handle the ambiguities inherent in natural language queries.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'Utilization of large language models has facilitated the creation of more intuitive and interactive Text2SQL systems, thereby enhancing their capability to manage the inherent ambiguities present in natural language queries, as observed in various empirical studies.'},\n",
       "  {'statement': 'For example, the system proposed by Li et al. (2020) employs a parser-independent interactive approach to enhance query understanding and disambiguation through step-by-step dialogue with the user.',\n",
       "   'related_sen_id': [4, 5],\n",
       "   'statement_hyde': 'A notable instance is the system introduced by Li et al. (2020), which adopts a parser-independent interactive methodology to augment query comprehension and disambiguation, achieved through a step-by-step dialogic interaction with the user, thereby improving overall system performance.'},\n",
       "  {'statement': 'Overall, the integration of LLMs into Text2SQL has opened up new avenues for research and development, paving the way for more sophisticated and powerful natural language interfaces to databases.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'In summary, the integration of large language models into Text2SQL frameworks has解锁ed novel research and development pathways, laying the groundwork for the creation of more advanced and potent natural language interfaces to databases, as corroborated by recent advancements in the field.'}],\n",
       " [{'statement': 'Data augmentation plays a crucial role in enhancing the performance and generalization capabilities of Text2SQL models.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'Recent studies have consistently demonstrated that data augmentation techniques are pivotal in improving the performance and generalization of Text2SQL models, as evidenced by enhanced accuracy and robustness across diverse datasets.'},\n",
       "  {'statement': 'Given the limited availability of labeled data for specific databases, techniques for synthesizing parallel datasets have gained significant attention.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'Due to the scarcity of labeled data for specialized databases, the development of methods for generating parallel datasets has attracted considerable research interest, as highlighted in several recent publications.'},\n",
       "  {'statement': 'One notable approach is the REFILL framework (Awasthi et al., 2023), which retrieves and edits text queries from existing schemas to generate diverse parallel datasets for adapting Text2SQL parsers to new schemas.',\n",
       "   'related_sen_id': [5],\n",
       "   'statement_hyde': 'The REFILL framework, introduced by Awasthi et al. (2023), represents a significant advancement by retrieving and modifying text queries from pre-existing schemas, thereby creating diverse parallel datasets essential for the adaptation of Text2SQL parsers to novel schemas.'},\n",
       "  {'statement': 'We show that retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the target schema, leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation methods.',\n",
       "   'related_sen_id': [8],\n",
       "   'statement_hyde': 'Our experimental results indicate that the process of retrieving diverse text, masking schema-specific tokens, and refilling with target schema-relevant tokens results in a notably higher diversity of text queries compared to conventional SQL-to-Text generation techniques.'},\n",
       "  {'statement': 'Through experiments spanning multiple databases, we demonstrate that fine-tuning parsers on datasets synthesized using REFILL consistently outperforms the prior data-augmentation methods.',\n",
       "   'related_sen_id': [9],\n",
       "   'statement_hyde': 'Extensive experiments across various databases have验证 that fine-tuning Text2SQL parsers on datasets generated via the REFILL method consistently yields superior performance over traditional data-augmentation techniques.'},\n",
       "  {'statement': 'Our approach is related to retrieve-and-edit models that have been used for semantic parsing (Hashimoto et al., 2018), dialogue generation (Chi et al., 2021), translation (Cai et al., 2021), and question answering (Karpukhin et al., 2020).',\n",
       "   'related_sen_id': [17, 18, 19, 20, 21],\n",
       "   'statement_hyde': 'Our methodology shares conceptual similarities with retrieve-and-edit models previously applied in various domains, including semantic parsing (as discussed by Hashimoto et al., 2018), dialogue generation (explored by Chi et al., 2021), translation (examined by Cai et al., 2021), and question answering (investigated by Karpukhin et al., 2020).'},\n",
       "  {'statement': \"However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work.\",\n",
       "   'related_sen_id': [22],\n",
       "   'statement_hyde': \"Nevertheless, our unique approach of conceptualizing the 'edit' phase as a two-step mask-and-fill process within a schema translation model distinguishes it from existing methodologies in the literature.\"},\n",
       "  {'statement': 'It then employs a schema translator model to convert the text of the training schema to the target schema, facilitating adaptation to new databases.',\n",
       "   'related_sen_id': [42],\n",
       "   'statement_hyde': 'The method subsequently utilizes a schema translator model to transform the text from the training schema to the target schema, thereby simplifying the adaptation process to new databases, as evidenced in recent adaptive learning frameworks.'},\n",
       "  {'statement': 'This method demonstrates consistent performance improvements over prior data augmentation techniques, highlighting the effectiveness of data-driven approaches for enhancing Text2SQL model adaptability.',\n",
       "   'related_sen_id': [43],\n",
       "   'statement_hyde': 'The approach exhibits consistent performance enhancements relative to previous data augmentation methods, underscoring the efficacy of data-driven strategies in augmenting the adaptability of Text2SQL models, as supported by comparative studies.'},\n",
       "  {'statement': 'Another relevant work is the study by Zhao et al. (2022), which emphasizes the importance of synthesizing high-quality data for Text2SQL parsing.',\n",
       "   'related_sen_id': [44, 45],\n",
       "   'statement_hyde': 'A pertinent study by Zhao et al. (2022) underscores the critical role of generating high-quality data in the context of Text2SQL parsing, aligning with the broader emphasis on data quality in natural language processing research.'},\n",
       "  {'statement': 'By incorporating techniques like data augmentation and synthetic data generation, researchers can effectively address the data scarcity challenge and improve the robustness of Text2SQL models.',\n",
       "   'related_sen_id': [52],\n",
       "   'statement_hyde': 'The integration of techniques such as data augmentation and synthetic data generation enables researchers to mitigate the challenge of data scarcity and enhance the robustness of Text2SQL models, as demonstrated in various empirical studies.'},\n",
       "  {'statement': 'In conclusion, data augmentation techniques have emerged as a vital component in advancing Text2SQL technology.',\n",
       "   'related_sen_id': [55],\n",
       "   'statement_hyde': 'In summary, data augmentation techniques have become indispensable in the progression of Text2SQL technology, as evidenced by their widespread adoption and significant impact on model performance.'},\n",
       "  {'statement': 'By synthesizing parallel datasets and leveraging large language models, researchers can enhance the adaptability and generalization capabilities of Text2SQL models, paving the way for more accurate and efficient natural language interfaces to databases.',\n",
       "   'related_sen_id': [56],\n",
       "   'statement_hyde': 'Through the synthesis of parallel datasets and the utilization of large language models, researchers can significantly boost the adaptability and generalization of Text2SQL models, thereby facilitating the development of more precise and efficient natural language interfaces for database interactions.'}],\n",
       " [{'statement': 'Ambiguity in natural language queries poses a significant challenge for Text2SQL systems.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'The presence of ambiguity within natural language queries has been widely recognized as a substantial obstacle in the development and efficacy of Text2SQL systems.'},\n",
       "  {'statement': 'For instance, the AmbiQT benchmark, which includes over 3000 examples where each text is interpretable as two plausible SQLs due to lexical and/or structural ambiguity, highlights this issue.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'Notably, the AmbiQT benchmark, comprising more than 3000 instances wherein each textual query can be interpreted as two valid SQL statements owing to lexical and structural ambiguities, underscores the prevalence of this challenge.'},\n",
       "  {'statement': 'This ambiguity can arise from various sources such as overlapping schema names, multiple confusing relationship paths, and the inherent ambiguity of natural language.',\n",
       "   'related_sen_id': [2],\n",
       "   'statement_hyde': 'Such ambiguities often originate from diverse sources, including overlapping schema names, numerous perplexing relationship paths, and the intrinsic ambiguity inherent in natural language.'},\n",
       "  {'statement': 'Furthermore, current Text-to-SQL systems and decoding algorithms, including those employing state-of-the-art LLMs, struggle to generate all valid interpretations for possible disambiguation by the user.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'Additionally, contemporary Text-to-SQL systems and decoding algorithms, even those leveraging state-of-the-art Large Language Models (LLMs), face difficulties in generating all plausible interpretations necessary for user-driven disambiguation.'},\n",
       "  {'statement': 'Users may express their information needs in various ways, leading to multiple valid interpretations and corresponding SQL queries.',\n",
       "   'related_sen_id': [4],\n",
       "   'statement_hyde': 'Users often articulate their information requirements in diverse manners, resulting in numerous valid interpretations and corresponding SQL query formulations.'},\n",
       "  {'statement': 'Addressing this ambiguity is crucial for achieving accurate and robust query generation.',\n",
       "   'related_sen_id': [5],\n",
       "   'statement_hyde': 'The resolution of this ambiguity is deemed essential for the attainment of precise and robust query generation capabilities.'},\n",
       "  {'statement': 'One approach to handling ambiguity is through interactive systems that engage users in a step-by-step dialogue to clarify their intent.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'An effective strategy for managing ambiguity involves the deployment of interactive systems that facilitate a step-by-step dialogue with users to elucidate their intentions.'},\n",
       "  {'statement': 'For example, the work by Stengel-Eskin et al. (2023) introduces A MP, a framework, dataset, and challenge for translating ambiguous natural language to formal representations like logic and code, which can be used in interactive systems to handle ambiguity.',\n",
       "   'related_sen_id': [7, 8],\n",
       "   'statement_hyde': 'For instance, Stengel-Eskin et al. (2023) have introduced A MP, a comprehensive framework, dataset, and challenge designed to translate ambiguous natural language into formal representations such as logic and code, thereby enhancing the capability of interactive systems to manage ambiguity.'},\n",
       "  {'statement': 'Additionally, the study by Zhao et al. (2021) proposes a generation system that addresses the cold-start zero-shot clarifying question challenge in conversational search, which is another example of interactive systems that engage users in a step-by-step dialogue to clarify their intent.',\n",
       "   'related_sen_id': [9, 10],\n",
       "   'statement_hyde': 'Moreover, Zhao et al. (2021) have proposed a generation system aimed at tackling the cold-start zero-shot clarifying question challenge within conversational search, exemplifying another instance of interactive systems that engage users in incremental dialogues to refine their intent.'},\n",
       "  {'statement': 'Furthermore, the research by Qian et al. (2022) focuses on resolving ambiguities in text-to-image generative models through a disambiguation framework that engages users in a step-by-step dialogue to clarify their intent.',\n",
       "   'related_sen_id': [11, 12],\n",
       "   'statement_hyde': 'Additionally, Qian et al. (2022) have concentrated on resolving ambiguities within text-to-image generative models by employing a disambiguation framework that involves users in a progressive dialogue to clarify their intentions.'},\n",
       "  {'statement': 'For instance, the system proposed by Li et al. (2020) employs a parser-independent interactive approach, allowing users to refine their queries based on feedback and disambiguate potential misunderstandings.',\n",
       "   'related_sen_id': [13, 14],\n",
       "   'statement_hyde': 'For example, the system proposed by Li et al. (2020) utilizes a parser-independent interactive approach, enabling users to refine their queries based on iterative feedback and to disambiguate potential misunderstandings.'},\n",
       "  {'statement': 'This interactive process enhances query understanding and improves the accuracy of the generated SQL queries.',\n",
       "   'related_sen_id': [15],\n",
       "   'statement_hyde': 'This interactive methodology serves to enhance query comprehension and subsequently improves the accuracy of the generated SQL queries.'},\n",
       "  {'statement': 'Another technique for addressing ambiguity is the use of disambiguation techniques within the model itself.',\n",
       "   'related_sen_id': [16],\n",
       "   'statement_hyde': 'An alternative method for tackling ambiguity involves the incorporation of disambiguation techniques directly within the model architecture.'},\n",
       "  {'statement': 'For instance, word sense disambiguation is one of the areas in NLP that has gained significant attention and numerous works have been proposed in this regards (Wang and Wang, 2021).',\n",
       "   'related_sen_id': [17],\n",
       "   'statement_hyde': 'For example, word sense disambiguation, a prominent area within Natural Language Processing (NLP), has attracted substantial attention, with numerous studies having been proposed in this domain (Wang and Wang, 2021).'},\n",
       "  {'statement': 'Resolving ambiguities in question answering (Min et al., 2020), conversational question answering (Guo et al., 2021), and task-oriented dialogue systems (Qian et al., 2022) has also been previously studied.',\n",
       "   'related_sen_id': [18, 19, 20, 21],\n",
       "   'statement_hyde': 'The resolution of ambiguities in various domains such as question answering (Min et al., 2020), conversational question answering (Guo et al., 2021), and task-oriented dialogue systems (Qian et al., 2022) has been the subject of prior research.'},\n",
       "  {'statement': 'Ambiguity resolution has also been studied in multi-modal applications, such as multi-modal machine translation (Li et al., 2022) or matching images or videos to disambiguated interpretation of a sentence (Berzak et al., 2015).',\n",
       "   'related_sen_id': [22, 23, 24],\n",
       "   'statement_hyde': 'The study of ambiguity resolution has extended into multi-modal applications, including multi-modal machine translation (Li et al., 2022) and the alignment of images or videos with disambiguated interpretations of sentences (Berzak et al., 2015).'},\n",
       "  {'statement': 'Despite those recent efforts, not much attention has been paid to ambiguities in text-to-image generative models.',\n",
       "   'related_sen_id': [25],\n",
       "   'statement_hyde': 'Despite these recent advancements, the issue of ambiguities in text-to-image generative models has received comparatively little attention.'},\n",
       "  {'statement': 'On the other hand, the growing popularity of those models, both in academic and non-academic circles, make it imperatives to better understand potential issues with those systems due to language ambiguity.',\n",
       "   'related_sen_id': [26],\n",
       "   'statement_hyde': 'Conversely, the increasing prevalence of these models within both academic and non-academic communities necessitates a deeper understanding of the potential challenges they face due to linguistic ambiguity.'},\n",
       "  {'statement': 'In this paper we have identified and addressed some of those issues.',\n",
       "   'related_sen_id': [27],\n",
       "   'statement_hyde': 'Within this paper, we have identified and addressed several of these pertinent issues.'},\n",
       "  {'statement': 'We hope that our work will inspire future effort on this important problem.',\n",
       "   'related_sen_id': [28],\n",
       "   'statement_hyde': 'It is our hope that the findings presented herein will stimulate further research endeavors aimed at tackling this significant problem.'},\n",
       "  {'statement': 'For example, the AmbiQT benchmark (Wang et al., 2022) introduces a dataset with ambiguous queries, each having two distinct valid SQL interpretations.',\n",
       "   'related_sen_id': [29],\n",
       "   'statement_hyde': 'For instance, the AmbiQT benchmark (Wang et al., 2022) introduces a dataset characterized by ambiguous queries, each of which possesses two distinct yet valid SQL interpretations.'},\n",
       "  {'statement': 'The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation.',\n",
       "   'related_sen_id': [30],\n",
       "   'statement_hyde': 'This benchmark is crafted through a hybrid approach, combining ChatGPT (OpenAI, 2022)-based synonym generation and perturbation with conventional rule-based perturbation techniques.'},\n",
       "  {'statement': 'AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs.',\n",
       "   'related_sen_id': [31],\n",
       "   'statement_hyde': 'The AmbiQT dataset encompasses over 3000 examples, each linking a natural language question pertaining to a database with two valid SQL queries.'},\n",
       "  {'statement': 'Inspired by our experience with several real-world datasets, we target four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity.',\n",
       "   'related_sen_id': [32],\n",
       "   'statement_hyde': 'Drawing inspiration from our extensive experience with multiple real-world datasets, we focus on four distinct types of ambiguity, encompassing both lexical ambiguities (such as ambiguous column and table names) and structural ambiguities (including the necessity of joins and the pre-computation of aggregates).'},\n",
       "  {'statement': 'This benchmark encourages the development of Text2SQL models capable of handling ambiguity by considering multiple interpretations and ranking them based on their relevance to the query.',\n",
       "   'related_sen_id': [33],\n",
       "   'statement_hyde': 'This benchmark is designed to foster the development of Text2SQL models that are adept at managing ambiguity by evaluating multiple interpretations and prioritizing them based on their relevance to the query.'},\n",
       "  {'statement': 'Furthermore, the work by Pourreza and Rafiei (2023) highlights the importance of cautious interpretation of benchmark evaluations.',\n",
       "   'related_sen_id': [34],\n",
       "   'statement_hyde': 'Additionally, the research conducted by Pourreza and Rafiei (2023) underscores the criticality of a cautious interpretation of benchmark evaluation results.'},\n",
       "  {'statement': 'They demonstrate that achieving perfect performance on existing benchmarks is unfeasible due to the inherent ambiguity in natural language queries.',\n",
       "   'related_sen_id': [35],\n",
       "   'statement_hyde': 'Their findings illustrate that attaining perfect performance on current benchmarks is inherently unattainable owing to the intrinsic ambiguity present in natural language queries.'},\n",
       "  {'statement': 'Their evaluation reveals that the true performance of Text2SQL models may be underestimated, emphasizing the need for additional independent evaluations and the consideration of multiple valid interpretations in benchmark design.',\n",
       "   'related_sen_id': [36],\n",
       "   'statement_hyde': 'Their evaluative analysis reveals that the actual performance of Text2SQL models might be significantly underestimated, thereby highlighting the necessity for supplementary independent evaluations and the incorporation of multiple valid interpretations in the design of benchmarks.'},\n",
       "  {'statement': 'In conclusion, addressing ambiguity in Text2SQL remains an active area of research.',\n",
       "   'related_sen_id': [37],\n",
       "   'statement_hyde': 'In summary, the addressing of ambiguity within Text2SQL continues to be a vibrant and ongoing area of research.'},\n",
       "  {'statement': 'Ambiguity in SQL has been studied in other fields of NLP, but it has been unexplored in the context of semantic parsing.',\n",
       "   'related_sen_id': [38],\n",
       "   'statement_hyde': 'While ambiguity in SQL has been examined within various domains of NLP, it remains largely unexplored within the specific context of semantic parsing.'},\n",
       "  {'statement': 'AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.',\n",
       "   'related_sen_id': [39],\n",
       "   'statement_hyde': 'AmbiQT stands as the inaugural open benchmark designed for assessing the coverage of ambiguous alternatives in the process of Text-to-SQL conversion.'},\n",
       "  {'statement': 'Interactive systems, disambiguation techniques, and careful interpretation of benchmark evaluations are essential for developing accurate and robust Text2SQL models capable of handling the complexities of natural language queries.',\n",
       "   'related_sen_id': [40],\n",
       "   'statement_hyde': 'The integration of interactive systems, advanced disambiguation techniques, and meticulous interpretation of benchmark evaluations is indispensable for the development of accurate and robust Text2SQL models that can effectively manage the intricacies of natural language queries.'}],\n",
       " [{'statement': 'The ethical implications of Text2SQL technology cannot be overlooked, especially considering its potential applications in sensitive domains like healthcare, finance, and government.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'Recent studies have underscored the ethical ramifications inherent in Text2SQL technology, particularly when deployed in critical sectors such as healthcare, finance, and governmental operations.'},\n",
       "  {'statement': 'While Text2SQL systems offer immense benefits by democratizing access to data, they also raise concerns regarding data privacy, fairness, and potential biases inherited from training data.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'Although Text2SQL systems significantly enhance data accessibility, they concurrently pose substantial challenges related to data privacy, equitable treatment, and the perpetuation of biases originating from their training datasets.'},\n",
       "  {'statement': 'For instance, systems trained on large-scale unfiltered data can suffer from degenerated and biased behavior, which can reflect and reinforce societal biases and structural inequalities.',\n",
       "   'related_sen_id': [2],\n",
       "   'statement_hyde': 'It has been observed that Text2SQL systems, when trained on extensive, uncurated datasets, may exhibit degraded and biased behaviors, thereby mirroring and potentially exacerbating existing societal prejudices and systemic disparities.'},\n",
       "  {'statement': 'Additionally, the risks associated with neural rendering studies, such as privacy and security issues linked to the capture of sensitive information, are also relevant to Text2SQL systems.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'Furthermore, the privacy and security risks identified in neural rendering research, particularly those pertaining to the acquisition of sensitive information, are equally pertinent to the functioning of Text2SQL systems.'},\n",
       "  {'statement': 'Recent studies, such as the work by Liu et al. (2023), have uncovered social biases in Text2SQL models, highlighting the need for careful consideration of the potential consequences of deploying these systems in real-world applications.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'Recent empirical investigations, including the seminal work by Liu et al. (2023), have revealed the presence of social biases within Text2SQL models, thereby emphasizing the necessity of a thorough evaluation of the potential real-world impacts of their deployment.'},\n",
       "  {'statement': 'Text-to-SQL models bridge the gap between database manipulation and amateur users and are mainly applied by administrative industries, such as banks, schools, and governments, which rely on AI-based applications to manipulate databases and further develop policies that will have profound impacts on various aspects of many people’s lives.',\n",
       "   'related_sen_id': [7],\n",
       "   'statement_hyde': 'Text-to-SQL models serve as a crucial interface between database operations and non-expert users, predominantly utilized by administrative sectors like banking, education, and governance, wherein AI-driven applications are instrumental in database management and the formulation of policies with far-reaching societal implications.'},\n",
       "  {'statement': 'Unfortunately, large pre-trained language models (PLMs) are actually acknowledged to contain social biases towards different demographics, and these wicked biases are observed to be inherited by downstream tasks.',\n",
       "   'related_sen_id': [9],\n",
       "   'statement_hyde': 'It is widely recognized that extensive pre-trained language models (PLMs) inherently harbor social biases against various demographic groups, and these insidious biases are subsequently propagated to downstream tasks, including Text2SQL applications.'},\n",
       "  {'statement': 'However, as we observed through experiments, social biases are integrally inherited by downstream models even fine-tuned on neutral data, as in the Text-to-SQL task.',\n",
       "   'related_sen_id': [11],\n",
       "   'statement_hyde': 'Experimental findings indicate that social biases persistently infiltrate downstream models, even when fine-tuned on ostensibly neutral datasets, a phenomenon evident in the Text-to-SQL task.'},\n",
       "  {'statement': 'These biases can manifest in various forms, including stereotypical correlations between judgmental expressions and different demographics, as well as incorrect comparisons that perpetuate harmful stereotypes.',\n",
       "   'related_sen_id': [12],\n",
       "   'statement_hyde': 'Such biases manifest diversely, encompassing stereotypical associations between evaluative language and specific demographics, alongside erroneous comparisons that reinforce detrimental stereotypes.'},\n",
       "  {'statement': 'The words for Middle-Eastern and Asian personas connect to critiques of Orientalism, a damaging depiction where the East (encompassing Asia and the Middle East) is represented as the “ultimate Other” against which Western culture is defined; inaccurate, romanticized representations of these cultures have historically been used as implicit justification for imperialism in these areas (Said, 1978; Ma, 2000; Yoshihara, 2002).',\n",
       "   'related_sen_id': [21],\n",
       "   'statement_hyde': \"The linguistic associations for Middle-Eastern and Asian personas resonate with critiques of Orientalism, a detrimental portrayal positioning the East (including Asia and the Middle East) as the 'ultimate Other' in contrast to Western culture. Historically, these misrepresentations and romanticized depictions have served as veiled rationalizations for imperialistic endeavors in these regions (Said, 1978; Ma, 2000; Yoshihara, 2002).\"},\n",
       "  {'statement': 'This reflects essentialism: individuals in these groups are defined solely by a limited, seemingly-fixed essential set of characteristics rather than their full humanity (Rosenblum and Travis, 1996; Woodward, 1997).',\n",
       "   'related_sen_id': [23],\n",
       "   'statement_hyde': 'This phenomenon exemplifies essentialism, wherein individuals from these groups are pigeonholed based on a narrow, ostensibly immutable set of traits, thereby negating their comprehensive human essence (Rosenblum and Travis, 1996; Woodward, 1997).'},\n",
       "  {'statement': 'To address these concerns, researchers have proposed several approaches. The BiaSpider benchmark (Liu et al., 2023) aims to uncover and categorize social biases in Text2SQL models by introducing a new paradigm for structured data bias measurement.',\n",
       "   'related_sen_id': [25, 26],\n",
       "   'statement_hyde': 'In response to these issues, the research community has devised multiple strategies. Notably, the BiaSpider benchmark, introduced by Liu et al. (2023), is designed to identify and classify social biases within Text2SQL models through an innovative framework for assessing biases in structured data.'},\n",
       "  {'statement': 'Additionally, the work by Awasthi et al. (2023) emphasizes the importance of reviewing Text2SQL systems for harmful biases before deployment and ensuring that users are aware of the potential for incorrect answers.',\n",
       "   'related_sen_id': [28, 29],\n",
       "   'statement_hyde': 'Furthermore, the research conducted by Awasthi et al. (2023) underscores the criticality of pre-deployment scrutiny of Text2SQL systems for detrimental biases, alongside ensuring user awareness regarding the likelihood of erroneous outputs.'},\n",
       "  {'statement': 'This highlights the need for responsible development and deployment of Text2SQL technology, with a focus on fairness, transparency, and accountability.',\n",
       "   'related_sen_id': [30],\n",
       "   'statement_hyde': 'These insights underscore the imperative for the conscientious development and deployment of Text2SQL technology, prioritizing fairness, transparency, and accountability in its implementation.'}],\n",
       " [{'statement': 'The evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al., 2018 ), SPIDER ( Yu et al., 2018 ), KaggleDBQA ( Lee et al., 2021 ), SEDE ( Hazoom et al., 2021 ), and EHRSQL ( Lee et al., 2022 ).',\n",
       "   'related_sen_id': [0, 1, 2, 3, 4, 5],\n",
       "   'statement_hyde': 'The assessment of Text2SQL models is critically dependent on the presence of extensive and varied benchmarks, including WikiSQL (introduced by Zhong et al., 2018), SPIDER (developed by Yu et al., 2018), KaggleDBQA (proposed by Lee et al., 2021), SEDE (created by Hazoom et al., 2021), and EHRSQL (designed by Lee et al., 2022).'},\n",
       "  {'statement': 'These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'Such benchmarks are instrumental in evaluating both the performance and robustness of Text2SQL models, and they also tackle the issue of ambiguity prevalent in real-world datasets.'},\n",
       "  {'statement': 'Dr. SPIDER ( Chang et al., 2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER.',\n",
       "   'related_sen_id': [7, 8],\n",
       "   'statement_hyde': 'Dr. SPIDER (introduced by Chang et al., 2023) serves as an additional benchmark aimed at evaluating the robustness of existing Text2SQL models through the perturbation of either the textual input or the schema within the SPIDER framework.'},\n",
       "  {'statement': 'Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges.',\n",
       "   'related_sen_id': [9],\n",
       "   'statement_hyde': 'A variety of notable benchmarks have arisen within the Text2SQL domain, each characterized by distinct features and inherent challenges.'},\n",
       "  {'statement': 'For instance, WikiSQL ( Zhong et al., 2018 ) and SPIDER ( Yu et al., 2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al., 2021 ), SEDE ( Hazoom et al., 2021 ), and EHRSQL ( Lee et al., 2022 ) aim to capture real-world scenarios.',\n",
       "   'related_sen_id': [10, 11, 12, 13, 14, 15],\n",
       "   'statement_hyde': 'For example, WikiSQL (developed by Zhong et al., 2018) and SPIDER (crafted by Yu et al., 2018) are widely-used benchmarks focusing on fundamental tasks, whereas benchmarks such as KaggleDBQA (proposed by Lee et al., 2021), SEDE (initiated by Hazoom et al., 2021), and EHRSQL (designed by Lee et al., 2022) are tailored to reflect real-world scenarios.'},\n",
       "  {'statement': 'The AmbiQT benchmark ( Wang et al., 2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.',\n",
       "   'related_sen_id': [18, 19],\n",
       "   'statement_hyde': 'The AmbiQT benchmark (introduced by Wang et al., 2022) stands as the inaugural open benchmark specifically designed to evaluate the coverage of ambiguous alternatives within the context of Text-to-SQL conversion.'},\n",
       "  {'statement': 'Additionally, Text2Analysis ( He et al., 2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.',\n",
       "   'related_sen_id': [20, 21],\n",
       "   'statement_hyde': 'Furthermore, Text2Analysis (developed by He et al., 2023) bridges the research gap pertaining to advanced analysis tasks and ambiguous queries within the realm of tabular data analysis.'},\n",
       "  {'statement': 'These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.',\n",
       "   'related_sen_id': [22],\n",
       "   'statement_hyde': 'Collectively, these benchmarks pose significant challenges in the field of tabular data analysis, thereby opening avenues for more sophisticated research endeavors.'},\n",
       "  {'statement': 'One of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database.',\n",
       "   'related_sen_id': [23],\n",
       "   'statement_hyde': 'Among the most extensively utilized benchmarks is WikiSQL (developed by Zhong et al., 2017), comprising natural language questions paired with corresponding SQL queries executed on a single database.'},\n",
       "  {'statement': 'WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models.',\n",
       "   'related_sen_id': [24],\n",
       "   'statement_hyde': 'WikiSQL emphasizes simple and direct queries, rendering it appropriate for assessing the fundamental capabilities of Text2SQL models.'},\n",
       "  {'statement': 'However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios.',\n",
       "   'related_sen_id': [25],\n",
       "   'statement_hyde': 'Nevertheless, the constrained scope and absence of complex queries in WikiSQL may fail to accurately represent the challenges prevalent in real-world scenarios.'},\n",
       "  {'statement': 'For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35%!',\n",
       "   'related_sen_id': [26],\n",
       "   'statement_hyde': 'For instance, the KITAB dataset, centered on literature-related queries, illustrates that even cutting-edge LLMs such as GPT4 and GPT3.5 face difficulties in constraint satisfaction, frequently generating irrelevant information, with the rate of fully correct responses significantly below 35%!'},\n",
       "  {'statement': 'Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios.',\n",
       "   'related_sen_id': [27],\n",
       "   'statement_hyde': 'Moreover, evaluating LLMs on intricate queries involving multiple constraint types and extended outputs remains a substantial challenge, given that numerous existing benchmarks have reached saturation and fail to offer a thorough evaluation of LLM performance in such contexts.'},\n",
       "  {'statement': 'To address this limitation, the Spider benchmark (Yu et al., 2018) was introduced.',\n",
       "   'related_sen_id': [28],\n",
       "   'statement_hyde': 'To mitigate this limitation, the Spider benchmark (developed by Yu et al., 2018) was introduced.'},\n",
       "  {'statement': 'Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas.',\n",
       "   'related_sen_id': [29],\n",
       "   'statement_hyde': 'Spider includes a varied array of complex and cross-domain questions, covering multiple databases characterized by diverse schemas.'},\n",
       "  {'statement': 'Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.',\n",
       "   'related_sen_id': [30],\n",
       "   'statement_hyde': 'Spider has emerged as a preferred benchmark for assessing the generalization capabilities of Text2SQL models.'},\n",
       "  {'statement': 'Another notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context.',\n",
       "   'related_sen_id': [31],\n",
       "   'statement_hyde': 'Another significant benchmark is SParC (introduced by Yu et al., 2019), which concentrates on cross-domain semantic parsing within a contextual framework.'},\n",
       "  {'statement': 'This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical.',\n",
       "   'related_sen_id': [32, 33, 34],\n",
       "   'statement_hyde': 'SParC addresses the shortcomings of preceding benchmarks by offering a large-scale dataset that encompasses multiple specific domains for Chinese passage retrieval, such as E-commerce, Entertainment video, and Medical.'},\n",
       "  {'statement': 'SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions.',\n",
       "   'related_sen_id': [35],\n",
       "   'statement_hyde': 'SParC introduces a more realistic setting through the inclusion of multi-turn dialogues and context-dependent questions.'},\n",
       "  {'statement': 'This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.',\n",
       "   'related_sen_id': [36],\n",
       "   'statement_hyde': 'This benchmark assesses the capability of Text2SQL models to retain context and produce accurate queries based on preceding interactions.'},\n",
       "  {'statement': 'AmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL.',\n",
       "   'related_sen_id': [37],\n",
       "   'statement_hyde': 'AmbiQT (developed by Wang et al., 2022) is a recent benchmark specifically designed to address the issue of ambiguity in Text2SQL.'},\n",
       "  {'statement': 'AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.',\n",
       "   'related_sen_id': [38, 39, 40, 41, 42],\n",
       "   'statement_hyde': 'AmbiQT comprises over 3000 examples, each linking a natural language question on a database with two valid SQL queries, and covers four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.'},\n",
       "  {'statement': 'When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution.',\n",
       "   'related_sen_id': [43],\n",
       "   'statement_hyde': 'In the presence of ambiguity, an ideal Text-toSQL system should include all valid alternatives within their top $k$ SQL outputs, allowing for user resolution.'},\n",
       "  {'statement': 'We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling.',\n",
       "   'related_sen_id': [44, 45, 46],\n",
       "   'statement_hyde': 'Our analysis reveals that current approaches, spanning from T5-3B to state-of-the-art models, are unable to generate all ambiguous outputs regardless of the decoding strategy employed, including beam search and diversity-enhancing sampling methods like Nucleus and Typical sampling.'},\n",
       "  {'statement': 'WikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions.',\n",
       "   'related_sen_id': [47],\n",
       "   'statement_hyde': 'WikiSQL, Spider, SParC, and AmbiQT each play a role in evaluating distinct facets of Text2SQL models, ranging from fundamental query generation to managing ambiguity and context-dependent queries.'},\n",
       "  {'statement': 'The landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning.',\n",
       "   'related_sen_id': [54],\n",
       "   'statement_hyde': 'The landscape of Text2SQL models has undergone substantial evolution, with a multitude of architectures and methodologies being investigated to address the intricacies of natural language understanding and database schema reasoning.'},\n",
       "  {'statement': 'One of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model.',\n",
       "   'related_sen_id': [56],\n",
       "   'statement_hyde': 'One of the pioneering and most impactful approaches to Text2SQL is the sequence-to-sequence model.'},\n",
       "  {'statement': 'This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries.',\n",
       "   'related_sen_id': [57],\n",
       "   'statement_hyde': 'This architecture, drawing inspiration from neural machine translation, utilizes an encoder-decoder framework to convert natural language questions into SQL queries.'},\n",
       "  {'statement': 'Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a).',\n",
       "   'related_sen_id': [59],\n",
       "   'statement_hyde': 'Prominent examples of sequence-to-sequence models within the Text2SQL domain include Seq2SQL (developed by Zhong et al., 2017) and RATSQL (introduced by Wang et al., 2020a).'},\n",
       "  {'statement': 'Graph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas.',\n",
       "   'related_sen_id': [61],\n",
       "   'statement_hyde': 'Graph-based models have garnered attention in the Text2SQL field owing to their capability to represent the structured nature of database schemas.'},\n",
       "  {'statement': 'These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation.',\n",
       "   'related_sen_id': [62],\n",
       "   'statement_hyde': 'These models leverage graph structures to encode the relationships among tables, columns, and cell values, facilitating more effective reasoning and query generation.'},\n",
       "  {'statement': 'Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema.',\n",
       "   'related_sen_id': [63],\n",
       "   'statement_hyde': 'Illustrative examples of graph-based models are GraphSQL (proposed by Yao et al., 2019) and Graphix-T5 (developed by Li et al., 2023b), which employ graph neural networks to capture the dependencies and relationships inherent in the database schema.'},\n",
       "  {'statement': 'Hybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths.',\n",
       "   'related_sen_id': [65],\n",
       "   'statement_hyde': 'Hybrid models integrate components from both sequence-to-sequence and graph-based models to harness their respective strengths.'},\n",
       "  {'statement': 'An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.',\n",
       "   'related_sen_id': [67],\n",
       "   'statement_hyde': 'A representative example of a hybrid model is RESDSQL (introduced by Li et al., 2023a), which decouples schema linking and skeleton parsing to enhance the accuracy and efficiency of Text2SQL systems.'},\n",
       "  {'statement': 'The integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness.',\n",
       "   'related_sen_id': [68],\n",
       "   'statement_hyde': 'The incorporation of LLMs into Text2SQL has transformative impacts on the field, providing unparalleled levels of language understanding and context-awareness.'},\n",
       "  {'statement': 'Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy.',\n",
       "   'related_sen_id': [69],\n",
       "   'statement_hyde': 'The fine-tuning of pre-trained LLMs such as BERT (developed by Devlin et al., 2019) and GPT (created by Radford et al., 2018) on Text2SQL tasks has resulted in substantial enhancements in query generation accuracy.'},\n",
       "  {'statement': 'Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.',\n",
       "   'related_sen_id': [70],\n",
       "   'statement_hyde': 'Models such as Grappa (proposed by Yu et al., 2020) and T5 (introduced by Raffel et al., 2020) exemplify the potential of LLMs in capturing the subtleties of natural language and producing accurate SQL queries.'},\n",
       "  {'statement': 'Interactive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries.',\n",
       "   'related_sen_id': [71],\n",
       "   'statement_hyde': 'Interactive Text2SQL systems are pivotal in addressing the inherent ambiguity present in natural language queries.'},\n",
       "  {'statement': 'These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings.',\n",
       "   'related_sen_id': [72],\n",
       "   'statement_hyde': 'These systems involve users in a step-by-step dialogue process to elucidate their intent and resolve potential ambiguities.'},\n",
       "  {'statement': 'An example of an interactive system is the one proposed by Li et al. (2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.',\n",
       "   'related_sen_id': [73, 74],\n",
       "   'statement_hyde': 'A notable example of an interactive system is the one proposed by Li et al. (2020), which utilizes a parser-independent interactive approach to augment query understanding and elevate the accuracy of generated SQL queries.'}],\n",
       " [{'statement': 'This is evident in the Text2Analysis benchmark, which addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis, providing a comprehensive taxonomy of advanced analysis and unclear queries, which enables the evaluation of the analytical abilities of large language models.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': \"The Text2Analysis benchmark, as demonstrated by Smith et al. (2022), effectively bridges the research gap in advanced analysis tasks and unclear queries within tabular data analysis. It offers a detailed taxonomy that facilitates the assessment of large language models' analytical capabilities.\"},\n",
       "  {'statement': 'Additionally, the evaluation of five state-of-the-art models on the Text2Analysis dataset reveals their strengths and weaknesses in handling advanced analysis tasks and unclear queries, providing valuable insights for future research.',\n",
       "   'related_sen_id': [2],\n",
       "   'statement_hyde': 'An evaluation conducted by Johnson and Lee (2021) on five leading models using the Text2Analysis dataset has elucidated their respective strengths and weaknesses in managing advanced analysis tasks and unclear queries, thereby offering critical insights for prospective research endeavors.'},\n",
       "  {'statement': 'However, the current evaluation metrics have limitations that need to be addressed to ensure a comprehensive and accurate assessment of model capabilities.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'Despite advancements, existing evaluation metrics, as highlighted by Brown and Green (2020), possess inherent limitations that necessitate addressing to achieve a thorough and precise assessment of model capabilities.'},\n",
       "  {'statement': 'For instance, comparisons are limited to publicly available checkpoints, which can lead to significant confounding variables due to differences in training recipes and datasets.',\n",
       "   'related_sen_id': [4],\n",
       "   'statement_hyde': 'As noted by Wang et al. (2019), the reliance on publicly available checkpoints for comparisons introduces significant confounding variables, stemming from variations in training recipes and datasets.'},\n",
       "  {'statement': \"Additionally, the focus on specific aspects of 3D awareness, such as single-image surface reconstruction and multiview consistency, may not provide a comprehensive understanding of a model's 3D capabilities.\",\n",
       "   'related_sen_id': [5],\n",
       "   'statement_hyde': \"The emphasis on particular facets of 3D awareness, like single-image surface reconstruction and multiview consistency, may fall short in offering a holistic comprehension of a model's 3D capabilities, as discussed by Zhang and Liu (2021).\"},\n",
       "  {'statement': \"Furthermore, the reliance on probing methods like linear probes and zero-shot analysis may not fully capture the model's ability to adapt to 3D tasks.\",\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': \"The dependence on probing techniques, such as linear probes and zero-shot analysis, may inadequately capture a model's adaptability to 3D tasks, as critiqued by Patel and Singh (2022).\"},\n",
       "  {'statement': 'This limitation can lead to underestimating the true performance of Text2SQL models, as demonstrated by Pourreza and Rafiei (2023).',\n",
       "   'related_sen_id': [11],\n",
       "   'statement_hyde': 'Pourreza and Rafiei (2023) have demonstrated that this limitation can result in an underestimation of the actual performance of Text2SQL models.'},\n",
       "  {'statement': 'It assumes that the reference queries are error-free and may not account for alternative valid queries that could also produce correct results.',\n",
       "   'related_sen_id': [14],\n",
       "   'statement_hyde': 'The assumption that reference queries are devoid of errors and the failure to consider alternative valid queries that could yield correct results have been critiqued by Davis and Miller (2021).'},\n",
       "  {'statement': 'Additionally, execution accuracy can be affected by ties in the database, where multiple rows satisfy the query conditions, leading to potential discrepancies in the evaluation results.',\n",
       "   'related_sen_id': [15],\n",
       "   'statement_hyde': 'As observed by Thompson et al. (2020), execution accuracy is susceptible to the influence of database ties, where multiple rows meet the query conditions, potentially causing discrepancies in evaluation outcomes.'},\n",
       "  {'statement': 'Semantic entropy improves over baselines in predicting whether a model’s answer to a question is correct.',\n",
       "   'related_sen_id': [24],\n",
       "   'statement_hyde': \"Research by Kumar and Gupta (2022) has shown that semantic entropy surpasses baseline methods in predicting the correctness of a model's answer to a question.\"},\n",
       "  {'statement': 'This can be achieved by leveraging techniques like query rewriting and normalization to identify semantically equivalent queries.',\n",
       "   'related_sen_id': [25],\n",
       "   'statement_hyde': 'The identification of semantically equivalent queries can be accomplished through the application of techniques such as query rewriting and normalization, as proposed by Lee and Chen (2021).'},\n",
       "  {'statement': 'Query rewriting aims to train a rewriting model to mimic human-rewritten queries, which can solve ambiguous problems and recover missing elements from the context.',\n",
       "   'related_sen_id': [26],\n",
       "   'statement_hyde': 'The objective of query rewriting is to train a model to emulate human-rewritten queries, thereby resolving ambiguities and retrieving missing contextual elements, as described by Martinez and Johnson (2020).'},\n",
       "  {'statement': 'Query expansion methods, such as selecting terms via the normalization score of their embeddings, can also enhance search queries and produce better retrieval results.',\n",
       "   'related_sen_id': [27],\n",
       "   'statement_hyde': 'Methods of query expansion, including the selection of terms based on the normalization score of their embeddings, have been shown to enhance search queries and improve retrieval results, as evidenced by Brown and Wilson (2019).'},\n",
       "  {'statement': 'Integrating both query rewriting and query expansion can reformulate better conversational queries.',\n",
       "   'related_sen_id': [28],\n",
       "   'statement_hyde': 'The integration of query rewriting and query expansion techniques has been demonstrated to reformulate more effective conversational queries, as highlighted by Taylor and Anderson (2021).'},\n",
       "  {'statement': \"For instance, the AmbigQA dataset measures a model’s ability to disambiguate-and-answer ambiguous questions, such as determining the specific game in the 'Fallout' series being referred to in a query like “Where does the new fallout game take place?” and then providing the correct location, “Appalachia”.\",\n",
       "   'related_sen_id': [30],\n",
       "   'statement_hyde': \"The AmbigQA dataset, as introduced by Garcia and Rodriguez (2022), assesses a model's capability to disambiguate and answer ambiguous questions, exemplified by identifying the specific 'Fallout' game in a query and providing the accurate location, 'Appalachia'.\"},\n",
       "  {'statement': 'Furthermore, SituatedQA focuses on temporal and geographic ambiguity, where additional time ranges and their corresponding answers are crowdsourced, and geographic questions are created by removing references to location and then crowdsourcing locations and corresponding answers.',\n",
       "   'related_sen_id': [31],\n",
       "   'statement_hyde': 'SituatedQA, as developed by Kim and Lee (2021), emphasizes temporal and geographic ambiguity, involving the crowdsourcing of additional time ranges and answers, as well as the creation of geographic questions by removing location references and subsequently crowdsourcing locations and corresponding answers.'},\n",
       "  {'statement': 'These datasets demonstrate the importance of accounting for ambiguity in natural language questions to improve model performance and calibration.',\n",
       "   'related_sen_id': [32],\n",
       "   'statement_hyde': 'The significance of addressing ambiguity in natural language questions for enhancing model performance and calibration is underscored by these datasets, as discussed by White and Black (2020).'},\n",
       "  {'statement': 'For example, in the context of multimodal fusion, it has been observed that increased data diversity can lead to substantial improvements in performance, especially in scarce data regimes.',\n",
       "   'related_sen_id': [34],\n",
       "   'statement_hyde': 'In the realm of multimodal fusion, it has been observed by Harris and Martin (2022) that enhancing data diversity can result in significant performance improvements, particularly in scenarios with limited data availability.'},\n",
       "  {'statement': 'Furthermore, fine-grained evaluation tests can be designed to assess specific model capabilities, such as understanding of ontology, logical equivalence, and answering under visual obfuscation.',\n",
       "   'related_sen_id': [35],\n",
       "   'statement_hyde': 'The design of fine-grained evaluation tests to assess specific model capabilities, including ontology understanding, logical equivalence, and performance under visual obfuscation, has been proposed by Nelson and Parker (2021).'},\n",
       "  {'statement': 'For instance, denotation accuracy, widely used in semantic parsing, is not directly applicable to tasks where tabular input encoding, reasoning, and generation are performed by the same model.',\n",
       "   'related_sen_id': [37],\n",
       "   'statement_hyde': 'Denotation accuracy, commonly employed in semantic parsing, is not directly applicable to tasks where a single model handles tabular input encoding, reasoning, and generation, as pointed out by Adams and Baker (2020).'},\n",
       "  {'statement': 'Additionally, the strict binary measure of table exact match may not be ideal for queries that do not impose ordering among columns or rows.',\n",
       "   'related_sen_id': [38],\n",
       "   'statement_hyde': 'The stringent binary measure of table exact match may be suboptimal for queries that do not require ordering among columns or rows, as critiqued by Green and Blue (2019).'},\n",
       "  {'statement': 'Furthermore, the limitations in training and benchmarking, as well as the need for more diverse and larger human evaluation, highlight the importance of exploring more evaluation approaches and metrics.',\n",
       "   'related_sen_id': [39],\n",
       "   'statement_hyde': 'The limitations inherent in training and benchmarking processes, coupled with the necessity for more diverse and extensive human evaluation, underscore the importance of investigating additional evaluation approaches and metrics, as emphasized by Clark and Lewis (2021).'},\n",
       "  {'statement': 'Training limitations include the constraint on model architecture and size, which may be improved by exploring specific architecture enhancements or larger models.',\n",
       "   'related_sen_id': [43],\n",
       "   'statement_hyde': 'Training limitations, such as constraints on model architecture and size, can potentially be mitigated through the exploration of specific architectural enhancements or the adoption of larger models, as suggested by Evans and Foster (2022).'},\n",
       "  {'statement': 'Benchmark limitations involve the scope of methods included and the calibration of dataset protocols, suggesting a need for a wider method spectrum and further work on aspects like the impact of the number of input frames.',\n",
       "   'related_sen_id': [44],\n",
       "   'statement_hyde': 'Benchmark limitations, pertaining to the scope of included methods and the calibration of dataset protocols, indicate a need for a broader range of methods and additional research on factors such as the impact of the number of input frames, as discussed by Miller and Davis (2020).'},\n",
       "  {'statement': 'Evaluation limitations highlight the need for a more diverse and larger pool of participants in human evaluations, as well as the exploration of additional evaluation approaches and metrics for a more holistic assessment of models.',\n",
       "   'related_sen_id': [45],\n",
       "   'statement_hyde': 'Evaluation limitations underscore the necessity for a more diverse and extensive pool of participants in human evaluations, along with the exploration of supplementary evaluation approaches and metrics to achieve a more comprehensive assessment of models, as highlighted by Thompson and Wilson (2021).'},\n",
       "  {'statement': 'These insights are drawn from studies that have examined the prevalent methods, representative datasets, and powerful benchmarks in the field, acknowledging that while progress has been made, there is still much to be done to refine evaluation metrics.',\n",
       "   'related_sen_id': [46],\n",
       "   'statement_hyde': 'These insights stem from comprehensive studies that have scrutinized prevalent methods, representative datasets, and influential benchmarks in the field, acknowledging that despite significant progress, further efforts are required to refine evaluation metrics, as concluded by Green and White (2022).'}],\n",
       " [{'statement': 'Combining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'The integration of Text2SQL with various NLP tasks has been identified as a promising avenue for enhancing natural language interfaces to databases, as suggested by recent advancements in the field.'},\n",
       "  {'statement': 'By integrating Text2SQL with tasks like question answering, information extraction, and natural language generation, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'Recent studies have demonstrated that the integration of Text2SQL with tasks such as question answering, information extraction, and natural language generation can lead to the development of more robust and versatile systems, which are adept at managing intricate user queries and delivering comprehensive information retrieval experiences.'},\n",
       "  {'statement': 'By combining Text2SQL with QA, we can build systems that can not only translate natural language questions into SQL queries but also answer those questions directly using the retrieved data.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'The combination of Text2SQL with question answering (QA) systems has shown potential in creating systems that not only convert natural language questions into SQL queries but also directly provide answers using the data retrieved, as evidenced by recent research.'},\n",
       "  {'statement': 'This integration can be achieved by incorporating QA models into the Text2SQL pipeline, allowing the system to generate natural language answers based on the results of the executed SQL queries.',\n",
       "   'related_sen_id': [4],\n",
       "   'statement_hyde': 'It has been proposed that the integration of QA models into the Text2SQL pipeline can facilitate the generation of natural language answers derived from the outcomes of executed SQL queries, thereby enhancing system functionality.'},\n",
       "  {'statement': 'By combining Text2SQL with IE, we can build systems that can extract structured information from unstructured text sources and store it in databases.',\n",
       "   'related_sen_id': [7],\n",
       "   'statement_hyde': 'The integration of Text2SQL with information extraction (IE) tasks enables the construction of systems capable of extracting structured information from unstructured text sources and subsequently storing this information in databases, as highlighted in recent literature.'},\n",
       "  {'statement': 'For instance, UniEX: an Effective and Efficient Framework for Unified Information Extraction Via a Span-extractive Perspective demonstrates the potential of using a unified extractive framework for various IE tasks, which can be beneficial for the Text2SQL pipeline.',\n",
       "   'related_sen_id': [9],\n",
       "   'statement_hyde': \"The UniEX framework, which employs a span-extractive perspective for unified information extraction, exemplifies the potential benefits of a unified approach to various IE tasks, thereby enhancing the Text2SQL pipeline's capabilities.\"},\n",
       "  {'statement': 'Additionally, the work on Benchmarking and Improving Text-to-SQL Generation under Ambiguity highlights the importance of addressing ambiguity in SQL generation, a critical aspect when integrating IE models into the Text2SQL process.',\n",
       "   'related_sen_id': [10],\n",
       "   'statement_hyde': 'Recent research on benchmarking and improving Text-to-SQL generation under ambiguity underscores the necessity of addressing ambiguity in SQL generation, particularly when integrating IE models into the Text2SQL process.'},\n",
       "  {'statement': 'Integrating Text2SQL with natural language generation (NLG) tasks can enable the generation of natural language explanations and summaries of query results.',\n",
       "   'related_sen_id': [12],\n",
       "   'statement_hyde': 'The integration of Text2SQL with natural language generation (NLG) tasks has been shown to facilitate the generation of natural language explanations and summaries of query results, thereby enhancing user comprehension.'},\n",
       "  {'statement': 'For example, the system proposed by Kokkalis et al. (2012) translates SQL queries into narratives, providing users with a more intuitive understanding of the query results.',\n",
       "   'related_sen_id': [15],\n",
       "   'statement_hyde': 'A notable example is the system proposed by Kokkalis et al. (2012), which translates SQL queries into narrative form, thereby offering users a more intuitive grasp of the query outcomes.'},\n",
       "  {'statement': 'By integrating Text2SQL with QA, IE, and NLG tasks, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.',\n",
       "   'related_sen_id': [17],\n",
       "   'statement_hyde': 'The integration of Text2SQL with QA, IE, and NLG tasks has the potential to yield more powerful and versatile systems, adept at managing complex user queries and delivering enriched information retrieval experiences, as supported by current research.'}],\n",
       " [{'statement': 'Addressing bias in Text2SQL systems is of paramount importance, especially considering their potential applications in sensitive domains like healthcare, finance, and government.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'The criticality of addressing bias in Text2SQL systems has been underscored by numerous studies, particularly given their burgeoning applications in sensitive sectors such as healthcare, finance, and governmental operations.'},\n",
       "  {'statement': 'Biased Text2SQL models can perpetuate and amplify existing stereotypes, leading to unfair and discriminatory outcomes.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'Research has consistently shown that biased Text2SQL models have the potential to not only perpetuate but also amplify pre-existing stereotypes, thereby resulting in outcomes that are both unfair and discriminatory.'},\n",
       "  {'statement': 'One approach to addressing bias is through the use of diverse and representative training data.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'An established method for mitigating bias in Text2SQL systems involves the utilization of training datasets that are both diverse and representative, thereby encompassing a wide array of perspectives and demographics.'},\n",
       "  {'statement': 'Techniques like data augmentation and synthetic data generation can be employed to create more diverse training datasets and improve the generalizability of Text2SQL models.',\n",
       "   'related_sen_id': [5],\n",
       "   'statement_hyde': 'It has been demonstrated that techniques such as data augmentation and the generation of synthetic data can significantly enhance the diversity of training datasets, thereby improving the generalizability of Text2SQL models.'},\n",
       "  {'statement': 'Another important strategy is to incorporate bias mitigation techniques during the model development process.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'A pivotal strategy in the development of Text2SQL models involves the integration of bias mitigation techniques, which are essential for ensuring the fairness of the resulting models.'},\n",
       "  {'statement': \"This can involve using techniques like adversarial training, which aims to minimize the model's reliance on biased features, or incorporating fairness constraints into the training objective.\",\n",
       "   'related_sen_id': [7],\n",
       "   'statement_hyde': \"Such strategies may include the application of adversarial training, designed to reduce the model's dependency on biased features, or the incorporation of fairness constraints within the training objectives, as evidenced by various studies.\"},\n",
       "  {'statement': 'These techniques can help ensure that the model treats different demographic groups fairly and avoids perpetuating harmful stereotypes.',\n",
       "   'related_sen_id': [8],\n",
       "   'statement_hyde': 'The implementation of these techniques has been shown to facilitate equitable treatment of diverse demographic groups by the model, thereby preventing the perpetuation of harmful stereotypes.'},\n",
       "  {'statement': 'Furthermore, it is crucial to evaluate Text2SQL models for bias and fairness before deployment.',\n",
       "   'related_sen_id': [9],\n",
       "   'statement_hyde': 'A critical step in the deployment process of Text2SQL models is the thorough evaluation of their bias and fairness, as emphasized by recent research in the field.'},\n",
       "  {'statement': 'By carefully evaluating and addressing bias, we can ensure that Text2SQL systems are fair, transparent, and accountable.',\n",
       "   'related_sen_id': [11],\n",
       "   'statement_hyde': 'Through meticulous evaluation and mitigation of bias, it is possible to guarantee that Text2SQL systems exhibit fairness, transparency, and accountability, as supported by extensive research.'},\n",
       "  {'statement': 'By incorporating diverse training data, bias mitigation techniques, and rigorous evaluation procedures, we can develop Text2SQL models that are not only accurate and efficient but also ethical and trustworthy.',\n",
       "   'related_sen_id': [13],\n",
       "   'statement_hyde': 'The integration of diverse training data, advanced bias mitigation techniques, and stringent evaluation protocols has been shown to yield Text2SQL models that are not only accurate and efficient but also uphold ethical standards and trustworthiness.'}],\n",
       " [{'statement': 'The field of Text2SQL is rapidly evolving, with numerous opportunities for future research and development.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'Recent advancements in the Text2SQL domain have been documented, indicating a swift progression and highlighting multiple avenues for prospective research endeavors.'},\n",
       "  {'statement': 'Integrating more advanced NLP techniques into Text2SQL models can significantly enhance their understanding of natural language and improve query generation accuracy.',\n",
       "   'related_sen_id': [2],\n",
       "   'statement_hyde': 'The incorporation of sophisticated Natural Language Processing (NLP) methodologies into Text2SQL frameworks has been shown to markedly augment the comprehension of natural language, thereby enhancing the precision of SQL query generation.'},\n",
       "  {'statement': 'For instance, the use of chain-of-thought prompting has been shown to improve performance on text-to-SQL parsing tasks, as demonstrated by the question-decomposition prompting method (QDecomp) which outperforms existing prompting methods by 2.4 and 1.5 point absolute gains on the development set of Spider and Spider Realistic datasets.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'For example, the application of chain-of-thought prompting techniques has been empirically validated to enhance text-to-SQL parsing performance, with the QDecomp method exhibiting superior results, achieving 2.4 and 1.5 point absolute improvements on the Spider and Spider Realistic development datasets, respectively.'},\n",
       "  {'statement': 'Combining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'The integration of Text2SQL with complementary NLP tasks, such as question answering, information extraction, and natural language generation, has the potential to yield more robust and adaptable computational systems.'},\n",
       "  {'statement': 'Ambiguity in SQL, arising from related column names, has been studied in (Wang et al., 2022), but they only consider column ambiguity.',\n",
       "   'related_sen_id': [11],\n",
       "   'statement_hyde': 'The issue of SQL ambiguity, particularly stemming from similar column names, has been explored by Wang et al. (2022); however, their research is limited to the examination of column-based ambiguities.'},\n",
       "  {'statement': 'To the best of our discernment, AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives.',\n",
       "   'related_sen_id': [13],\n",
       "   'statement_hyde': 'To our knowledge, AmbiQT is the inaugural open benchmark designed to evaluate the coverage of ambiguous SQL query alternatives.'},\n",
       "  {'statement': 'Exploring techniques like interactive systems, disambiguation methods, and domain adaptation can help models better handle these challenges and improve their performance in real-world scenarios.',\n",
       "   'related_sen_id': [17],\n",
       "   'statement_hyde': 'Investigating methodologies such as interactive systems, disambiguation techniques, and domain adaptation strategies can facilitate improved handling of these challenges, thereby enhancing model performance in practical, real-world applications.'},\n",
       "  {'statement': 'Additionally, investigating the use of transfer learning and few-shot learning can enable models to quickly adapt to new domains and tasks with limited training data.',\n",
       "   'related_sen_id': [21],\n",
       "   'statement_hyde': 'Furthermore, the exploration of transfer learning and few-shot learning approaches can empower models to rapidly acclimate to novel domains and tasks, even when constrained by limited training data.'},\n",
       "  {'statement': 'Continuing to address ethical considerations and bias mitigation in Text2SQL systems is essential for building fair and responsible natural language interfaces to databases.',\n",
       "   'related_sen_id': [26],\n",
       "   'statement_hyde': 'The ongoing focus on ethical considerations and the mitigation of biases within Text2SQL systems is crucial for the development of equitable and responsible natural language interfaces for database interactions.'}],\n",
       " [{'statement': 'The Text2SQL task has seen significant advancements in recent years, driven by the integration of deep learning, large language models, and interactive systems.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'Recent years have witnessed substantial progress in the Text2SQL task, primarily attributed to the incorporation of deep learning methodologies, the application of large language models, and the utilization of interactive system frameworks.'},\n",
       "  {'statement': 'This research survey has provided a comprehensive overview of the current state of Text2SQL technology, exploring its evolution, key benchmarks and models, limitations, and future directions.',\n",
       "   'related_sen_id': [1],\n",
       "   'statement_hyde': 'This survey offers an extensive review of the present landscape of Text2SQL technology, delving into its historical development, significant benchmarks and models, existing limitations, and prospective research avenues.'},\n",
       "  {'statement': 'The Text2Analysis benchmark is proposed as a new benchmark to further explore LLMs’ upper limits in challenging tabular data analysis tasks.',\n",
       "   'related_sen_id': [2],\n",
       "   'statement_hyde': 'A novel benchmark, termed Text2Analysis, has been introduced with the aim of investigating the upper performance boundaries of large language models in the context of demanding tabular data analysis tasks.'},\n",
       "  {'statement': 'We have presented the Text2Analysis dataset that addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.',\n",
       "   'related_sen_id': [3],\n",
       "   'statement_hyde': 'The Text2Analysis dataset has been introduced to bridge the existing research gap pertaining to sophisticated analysis tasks and ambiguous query handling within the domain of tabular data analysis.'},\n",
       "  {'statement': 'A Text-to-SQL model takes as input a question expressed as a natural language text x, and a database schema comprising of table and column names, and outputs an SQL program y which can be executed against the database to answer the user’s question.',\n",
       "   'related_sen_id': [4],\n",
       "   'statement_hyde': 'A Text-to-SQL model operates by receiving a natural language query (denoted as x) and a database schema that includes table and column names, subsequently generating an SQL program (denoted as y) that can be executed on the database to retrieve the desired information.'},\n",
       "  {'statement': 'In this paper, we propose to uncover and categorize social biases in the Text-to-SQL task.',\n",
       "   'related_sen_id': [5],\n",
       "   'statement_hyde': 'This paper aims to identify and systematically categorize the presence of social biases within the Text-to-SQL task, thereby addressing a critical aspect of model fairness and accuracy.'},\n",
       "  {'statement': 'The survey began by discussing the background and related work, highlighting the early developments in Text2SQL, the impact of deep learning and large language models, and techniques for data augmentation and ambiguity handling.',\n",
       "   'related_sen_id': [6],\n",
       "   'statement_hyde': 'The initial sections of the survey provide a foundational background and review of related work, emphasizing the pioneering advancements in Text2SQL, the transformative influence of deep learning and large language models, as well as methodologies for data augmentation and the management of query ambiguities.'},\n",
       "  {'statement': 'It then delved into the current benchmarks and models, analyzing popular benchmarks like WikiSQL and Spider, and examining different Text2SQL models, including sequence-to-sequence models, graph-based models, and hybrid models.',\n",
       "   'related_sen_id': [7],\n",
       "   'statement_hyde': 'Subsequent sections delve into an analysis of contemporary benchmarks and models, scrutinizing widely-used benchmarks such as WikiSQL and Spider, and exploring various Text2SQL model architectures, including sequence-to-sequence, graph-based, and hybrid approaches.'},\n",
       "  {'statement': 'The survey continued by discussing the limitations of current Text2SQL systems and proposing potential solutions and future research directions.',\n",
       "   'related_sen_id': [8],\n",
       "   'statement_hyde': 'The survey progresses to identify the limitations inherent in current Text2SQL systems, proposing viable solutions and outlining prospective research trajectories to address these challenges.'},\n",
       "  {'statement': 'This included a critical analysis of evaluation metrics, the potential for combining Text2SQL with other NLP tasks, methods for addressing bias, and new research directions for advancing Text2SQL technology.',\n",
       "   'related_sen_id': [9],\n",
       "   'statement_hyde': 'This encompassed a thorough examination of evaluation metrics, the feasibility of integrating Text2SQL with other natural language processing tasks, strategies for mitigating bias, and novel research pathways aimed at furthering Text2SQL technological advancements.'},\n",
       "  {'statement': 'The survey concludes by summarizing the key findings and highlighting the potential impact of Text2SQL technology.',\n",
       "   'related_sen_id': [10],\n",
       "   'statement_hyde': 'The concluding section of the survey synthesizes the principal findings and underscores the transformative potential of Text2SQL technology in various application domains.'},\n",
       "  {'statement': 'The integration of neural networks, LLMs, and interactive systems has revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.',\n",
       "   'related_sen_id': [11],\n",
       "   'statement_hyde': 'The amalgamation of neural network architectures, large language models, and interactive system designs has brought about a paradigm shift in the field, resulting in the development of Text2SQL systems that are notably more accurate, robust, and user-centric.'},\n",
       "  {'statement': 'However, challenges remain in addressing ambiguity, bias, and real-world complexities.',\n",
       "   'related_sen_id': [12],\n",
       "   'statement_hyde': 'Despite these advancements, significant challenges persist in effectively addressing issues of query ambiguity, inherent biases, and the complexities encountered in real-world application scenarios.'},\n",
       "  {'statement': 'For instance, preferences and values are not universal, and they are often inconsistently defined.',\n",
       "   'related_sen_id': [13],\n",
       "   'statement_hyde': 'For example, it is observed that preferences and values exhibit non-universality and are frequently subject to inconsistent definitions across different contexts.'},\n",
       "  {'statement': \"Additionally, human feedback is inherently incomplete, and operationalizing a 'good' output is difficult.\",\n",
       "   'related_sen_id': [14],\n",
       "   'statement_hyde': \"Furthermore, the nature of human feedback is inherently limited in its comprehensiveness, and the process of defining and operationalizing what constitutes a 'good' output remains a complex and challenging task.\"},\n",
       "  {'statement': 'Furthermore, crowdworkers and social media users are neither representative nor sufficient, which can lead to biased outcomes.',\n",
       "   'related_sen_id': [15],\n",
       "   'statement_hyde': 'Moreover, the reliance on crowdworkers and social media users for data collection is problematic, as these groups are often neither representative of the broader population nor sufficient in number, thereby potentially introducing biases into the outcomes.'},\n",
       "  {'statement': 'Future research directions include exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations.',\n",
       "   'related_sen_id': [16],\n",
       "   'statement_hyde': 'Prospective research avenues involve the exploration of sophisticated natural language processing techniques, the integration of Text2SQL with complementary tasks, the tackling of real-world application challenges, and a heightened emphasis on ethical considerations to ensure responsible technological advancement.'},\n",
       "  {'statement': 'By continuing to advance Text2SQL technology, we can unlock its full potential for empowering users to access and analyze data more effectively.',\n",
       "   'related_sen_id': [17],\n",
       "   'statement_hyde': 'The ongoing advancement of Text2SQL technology holds the promise of fully realizing its potential to empower users with enhanced capabilities for data access and analysis, thereby fostering greater data-driven decision-making.'}]]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = await process_all_sections(new_sections, find_statementer, topic)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.\n",
      "[0]\n",
      "\n",
      "## 1 Introduction\n",
      "\n",
      "Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.\n",
      "However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.\n",
      "[1]\n",
      "However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.\n",
      "This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.\n",
      "[4]\n",
      "This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.\n",
      "This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.\n",
      "[5]\n",
      "This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.\n",
      "To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.\n",
      "[6]\n",
      "To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.\n",
      "Benchmarks like PredBench have limitations in terms of training, benchmarking, and evaluation.\n",
      "[10]\n",
      "Benchmarks like PredBench [PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines, 66908e2301d2a3fbfcea14d7, 8] have limitations in terms of training, benchmarking, and evaluation.\n",
      "For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.\n",
      "[11]\n",
      "For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.\n",
      "Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.\n",
      "[12]\n",
      "Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.\n",
      "To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.\n",
      "[13]\n",
      "To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.\n",
      "Additionally, incorporating indicators of attack failures could help in debugging faulty evaluations and lead to fairer assessments.\n",
      "[14]\n",
      "Additionally, incorporating indicators of attack failures [Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples, 60d140d191e011c16f0cb388, 5] could help in debugging faulty evaluations and lead to fairer assessments.\n",
      "Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments to evaluate models' ability to exhibit rational behavior in economic tasks.\n",
      "[15]\n",
      "Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments [STEER: Assessing the Economic Rationality of Large Language Models, 65cec1c1939a5f40828f00d7, 11] to evaluate models' ability to exhibit rational behavior in economic tasks.\n",
      "The AmbiQT benchmark addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.\n",
      "[17]\n",
      "For example, the AmbiQT benchmark (Benchmarking and Improving Text-to-SQL Generation under Ambiguity, 6535d747939a5f408295c649, 1) addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.\n",
      "The work on interactive Text-to-SQL generation proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.\n",
      "[18]\n",
      "Furthermore, the work on interactive Text-to-SQL generation (Interactive Text-to-SQL Generation Via Editable Step-by-Step Explanations, 6461b9c9d68f896efad43133, 1) proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.\n",
      "The exploration of chain-of-thought style prompting for Text-to-SQL aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.\n",
      "[19]\n",
      "Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL (Exploring Chain-of-Thought Style Prompting for Text-to-SQL, 646d8642d68f896efa0a3040, 1) aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.\n",
      "We also address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.\n",
      "[20]\n",
      "Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.\n",
      "Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.\n",
      "[21]\n",
      "Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.\n",
      "The early approaches to Text2SQL can be categorized into rule-based systems and grammar-based methods.\n",
      "[0]\n",
      "\n",
      "## 2.1 Early Developments\n",
      "\n",
      "The early approaches to Text2SQL can be categorized into rule-based systems and grammar-based methods.\n",
      "Rule-based systems, such as the one proposed by Hendrix et al. (1978), relied on handcrafted rules to map natural language questions to SQL queries.\n",
      "[1, 2]\n",
      "Rule-based systems, such as the one proposed by Hendrix et al.\n",
      "(1978), relied on handcrafted rules to map natural language questions to SQL queries.\n",
      "These systems were limited in their ability to handle complex queries and required extensive manual effort to create and maintain the rules.\n",
      "[9]\n",
      "These systems were limited in their ability to handle complex queries and required extensive manual effort to create and maintain the rules.\n",
      "Grammar-based methods, like the one developed by Giordani and Moschitti (2012), used generative parsers to translate questions into SQL queries.\n",
      "[10]\n",
      "Grammar-based methods, like the one developed by Giordani and Moschitti (2012), used generative parsers to translate questions into SQL queries.\n",
      "While these methods offered some flexibility, they still struggled with the inherent complexity and ambiguity of natural language.\n",
      "[11]\n",
      "While these methods offered some flexibility, they still struggled with the inherent complexity and ambiguity of natural language.\n",
      "For instance, the approach necessitates meticulous prompt design to generate exercises, which inevitably entails human intervention, introducing potential bias or variability and may not scale efficiently.\n",
      "[12]\n",
      "For instance, the approach necessitates meticulous prompt design to generate exercises, which inevitably entails human intervention, introducing potential bias or variability and may not scale efficiently.\n",
      "Additionally, the quality and correctness of the generated problems are not explicitly addressed, and the current framework relies on a source problem for exercise generation, limiting flexibility and robustness.\n",
      "[13]\n",
      "Additionally, the quality and correctness of the generated problems are not explicitly addressed, and the current framework relies on a source problem for exercise generation, limiting flexibility and robustness.\n",
      "Furthermore, the handling of ambiguity in natural language is a significant challenge, as models often fail to capture the distribution of possible meanings without deliberate instruction.\n",
      "[14]\n",
      "Furthermore, the handling of ambiguity in natural language is a significant challenge, as models often fail to capture the distribution of possible meanings without deliberate instruction.\n",
      "The advent of deep learning brought about a paradigm shift in the field of Text2SQL, enabling the construction of several large text-to-SQL datasets, such as WikiSQL (Zhong et al., 2017) and Spider (Yu et al., 2018), and achieving unprecedented performance in recent years (Rubin and Berant, 2021; Wang et al., 2020a; Scholak et al., 2021; Yu et al., 2020; Hwang et al., 2019).\n",
      "[0]\n",
      "\n",
      "## 2.2 Deep Learning Era\n",
      "\n",
      "The advent of deep learning brought about a paradigm shift in the field of Text2SQL, enabling the construction of several large text-to-SQL datasets, such as WikiSQL (Zhong et al., 2017) and Spider (Yu et al., 2018), and achieving unprecedented performance in recent years (Rubin and Berant, 2021; Wang et al., 2020a; Scholak et al., 2021; Yu et al., 2020; Hwang et al., 2019).\n",
      "Neural network-based models, particularly sequence-to-sequence models, demonstrated remarkable improvements in translation accuracy and generalization capabilities.\n",
      "[1]\n",
      "Neural network-based models, particularly sequence-to-sequence models, demonstrated remarkable improvements in translation accuracy and generalization capabilities.\n",
      "Notable examples include Seq2SQL (Zhong et al., 2017), which employed reinforcement learning to generate SQL queries, and RATSQL (Wang et al., 2020a), which introduced a relation-aware self-attention mechanism to better encode the relationships between columns and tables.\n",
      "[2]\n",
      "Notable examples include Seq2SQL (Zhong et al., 2017), which employed reinforcement learning to generate SQL queries, and RATSQL (Wang et al., 2020a), which introduced a relation-aware self-attention mechanism to better encode the relationships between columns and tables.\n",
      "These models leveraged the power of deep learning to capture the complexities of natural language and database schemas, leading to more accurate and robust Text2SQL systems.\n",
      "[3]\n",
      "These models leveraged the power of deep learning to capture the complexities of natural language and database schemas, leading to more accurate and robust Text2SQL systems.\n",
      "Furthermore, the integration of large language models (LLMs) like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) into Text2SQL further pushed the boundaries of performance.\n",
      "[4]\n",
      "Furthermore, the integration of large language models (LLMs) like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) into Text2SQL further pushed the boundaries of performance.\n",
      "These pre-trained models, fine-tuned on Text2SQL tasks, demonstrated superior understanding of language semantics and context, resulting in more accurate query generation.\n",
      "[5]\n",
      "These pre-trained models, fine-tuned on Text2SQL tasks, demonstrated superior understanding of language semantics and context, resulting in more accurate query generation.\n",
      "For instance, Grappa (Yu et al., 2020) combined grammar-augmented pre-training with table semantic parsing, showcasing the potential of LLMs in Text2SQL.\n",
      "[6]\n",
      "For instance, Grappa (Yu et al., 2020) combined grammar-augmented pre-training with table semantic parsing, showcasing the potential of LLMs in Text2SQL.\n",
      "The deep learning era also witnessed the emergence of interactive Text2SQL systems, which aimed to address the ambiguity inherent in natural language queries.\n",
      "[7]\n",
      "The deep learning era also witnessed the emergence of interactive Text2SQL systems, which aimed to address the ambiguity inherent in natural language queries.\n",
      "These systems, such as the one proposed by Li et al. (2020), employed parser-independent interactive approaches to enhance query understanding and disambiguation.\n",
      "[8, 9]\n",
      "These systems, such as the one proposed by Li et al.\n",
      "(2020), employed parser-independent interactive approaches to enhance query understanding and disambiguation.\n",
      "By engaging users in a step-by-step dialogue, these systems could clarify ambiguities and generate more accurate SQL queries.\n",
      "[10]\n",
      "By engaging users in a step-by-step dialogue, these systems could clarify ambiguities and generate more accurate SQL queries.\n",
      "In summary, the deep learning era marked a significant leap forward in Text2SQL technology.\n",
      "[11]\n",
      "In summary, the deep learning era marked a significant leap forward in Text2SQL technology.\n",
      "The integration of neural networks, LLMs, and interactive systems revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.\n",
      "[12]\n",
      "The integration of neural networks, LLMs, and interactive systems revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.\n",
      "The integration of large language models (LLMs) into Text2SQL has significantly advanced the field.\n",
      "[0]\n",
      "\n",
      "## 2.3 Large Language Models\n",
      "\n",
      "The integration of large language models (LLMs) into Text2SQL has significantly advanced the field.\n",
      "LLMs, such as BERT (Devlin et al., 2019) and GPT (Radford et al., 2018), have demonstrated remarkable capabilities in understanding language semantics and context, leading to more accurate and robust query generation.\n",
      "[1]\n",
      "LLMs, such as BERT (Devlin et al., 2019) and GPT (Radford et al., 2018), have demonstrated remarkable capabilities in understanding language semantics and context, leading to more accurate and robust query generation.\n",
      "Fine-tuning these pre-trained models on Text2SQL tasks has proven to be highly effective, as evidenced by the success of models like Grappa (Yu et al., 2020), which combines grammar-augmented pre-training with table semantic parsing.\n",
      "[2]\n",
      "Fine-tuning these pre-trained models on Text2SQL tasks has proven to be highly effective, as evidenced by the success of models like Grappa (Yu et al., 2020), which combines grammar-augmented pre-training with table semantic parsing.\n",
      "The use of LLMs has also enabled the development of more user-friendly and interactive Text2SQL systems, which can better handle the ambiguities inherent in natural language queries.\n",
      "[3]\n",
      "The use of LLMs has also enabled the development of more user-friendly and interactive Text2SQL systems, which can better handle the ambiguities inherent in natural language queries.\n",
      "For example, the system proposed by Li et al. (2020) employs a parser-independent interactive approach to enhance query understanding and disambiguation through step-by-step dialogue with the user.\n",
      "[4, 5]\n",
      "For example, the system proposed by Li et al.\n",
      "(2020) employs a parser-independent interactive approach to enhance query understanding and disambiguation through step-by-step dialogue with the user.\n",
      "Overall, the integration of LLMs into Text2SQL has opened up new avenues for research and development, paving the way for more sophisticated and powerful natural language interfaces to databases.\n",
      "[6]\n",
      "Overall, the integration of LLMs into Text2SQL has opened up new avenues for research and development, paving the way for more sophisticated and powerful natural language interfaces to databases.\n",
      "Data augmentation plays a crucial role in enhancing the performance and generalization capabilities of Text2SQL models.\n",
      "[0]\n",
      "\n",
      "## 2.4 Data Augmentation\n",
      "\n",
      "Data augmentation plays a crucial role in enhancing the performance and generalization capabilities of Text2SQL models.\n",
      "Given the limited availability of labeled data for specific databases, techniques for synthesizing parallel datasets have gained significant attention.\n",
      "[1]\n",
      "Given the limited availability of labeled data for specific databases, techniques for synthesizing parallel datasets have gained significant attention.\n",
      "One notable approach is the REFILL framework (Awasthi et al., 2023), which retrieves and edits text queries from existing schemas to generate diverse parallel datasets for adapting Text2SQL parsers to new schemas.\n",
      "[5]\n",
      "One notable approach is the REFILL framework (Awasthi et al., 2023), which retrieves and edits text queries from existing schemas to generate diverse parallel datasets for adapting Text2SQL parsers to new schemas.\n",
      "We show that retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the target schema, leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation methods.\n",
      "[8]\n",
      "We show that retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the target schema, leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation methods.\n",
      "Through experiments spanning multiple databases, we demonstrate that fine-tuning parsers on datasets synthesized using REFILL consistently outperforms the prior data-augmentation methods.\n",
      "[9]\n",
      "Through experiments spanning multiple databases, we demonstrate that fine-tuning parsers on datasets synthesized using REFILL consistently outperforms the prior data-augmentation methods.\n",
      "Our approach is related to retrieve-and-edit models that have been used for semantic parsing (Hashimoto et al., 2018), dialogue generation (Chi et al., 2021), translation (Cai et al., 2021), and question answering (Karpukhin et al., 2020).\n",
      "[17, 18, 19, 20, 21]\n",
      "Our approach is related to retrieve-and-edit models that have been used for semantic parsing ( Hashimoto et al.\n",
      ",2018 ), dialogue generation ( Chi et al.\n",
      ",2021 ), translation ( Cai et al.\n",
      ",2021 ), and question answering ( Karpukhin et al.\n",
      ",2020 ).\n",
      "However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work.\n",
      "[22]\n",
      "However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work.\n",
      "It then employs a schema translator model to convert the text of the training schema to the target schema, facilitating adaptation to new databases.\n",
      "[42]\n",
      "It then employs a schema translator model to convert the text of the training schema to the target schema, facilitating adaptation to new databases.\n",
      "This method demonstrates consistent performance improvements over prior data augmentation techniques, highlighting the effectiveness of data-driven approaches for enhancing Text2SQL model adaptability.\n",
      "[43]\n",
      "This method demonstrates consistent performance improvements over prior data augmentation techniques, highlighting the effectiveness of data-driven approaches for enhancing Text2SQL model adaptability.\n",
      "Another relevant work is the study by Zhao et al. (2022), which emphasizes the importance of synthesizing high-quality data for Text2SQL parsing.\n",
      "[44, 45]\n",
      "Another relevant work is the study by Zhao et al.\n",
      "(2022), which emphasizes the importance of synthesizing high-quality data for Text2SQL parsing.\n",
      "By incorporating techniques like data augmentation and synthetic data generation, researchers can effectively address the data scarcity challenge and improve the robustness of Text2SQL models.\n",
      "[52]\n",
      "By incorporating techniques like data augmentation and synthetic data generation, researchers can effectively address the data scarcity challenge and improve the robustness of Text2SQL models.\n",
      "In conclusion, data augmentation techniques have emerged as a vital component in advancing Text2SQL technology.\n",
      "[55]\n",
      "In conclusion, data augmentation techniques have emerged as a vital component in advancing Text2SQL technology.\n",
      "By synthesizing parallel datasets and leveraging large language models, researchers can enhance the adaptability and generalization capabilities of Text2SQL models, paving the way for more accurate and efficient natural language interfaces to databases.\n",
      "[56]\n",
      "By synthesizing parallel datasets and leveraging large language models, researchers can enhance the adaptability and generalization capabilities of Text2SQL models, paving the way for more accurate and efficient natural language interfaces to databases.\n",
      "Ambiguity in natural language queries poses a significant challenge for Text2SQL systems.\n",
      "[0]\n",
      "\n",
      "## 2.5 Addressing Ambiguity\n",
      "\n",
      "Ambiguity in natural language queries poses a significant challenge for Text2SQL systems.\n",
      "For instance, the AmbiQT benchmark, which includes over 3000 examples where each text is interpretable as two plausible SQLs due to lexical and/or structural ambiguity, highlights this issue.\n",
      "[1]\n",
      "For instance, the AmbiQT benchmark, which includes over 3000 examples where each text is interpretable as two plausible SQLs due to lexical and/or structural ambiguity, highlights this issue.\n",
      "This ambiguity can arise from various sources such as overlapping schema names, multiple confusing relationship paths, and the inherent ambiguity of natural language.\n",
      "[2]\n",
      "This ambiguity can arise from various sources such as overlapping schema names, multiple confusing relationship paths, and the inherent ambiguity of natural language.\n",
      "Furthermore, current Text-to-SQL systems and decoding algorithms, including those employing state-of-the-art LLMs, struggle to generate all valid interpretations for possible disambiguation by the user.\n",
      "[3]\n",
      "Furthermore, current Text-to-SQL systems and decoding algorithms, including those employing state-of-the-art LLMs, struggle to generate all valid interpretations for possible disambiguation by the user.\n",
      "Users may express their information needs in various ways, leading to multiple valid interpretations and corresponding SQL queries.\n",
      "[4]\n",
      "Users may express their information needs in various ways, leading to multiple valid interpretations and corresponding SQL queries.\n",
      "Addressing this ambiguity is crucial for achieving accurate and robust query generation.\n",
      "[5]\n",
      "Addressing this ambiguity is crucial for achieving accurate and robust query generation.\n",
      "One approach to handling ambiguity is through interactive systems that engage users in a step-by-step dialogue to clarify their intent.\n",
      "[6]\n",
      "One approach to handling ambiguity is through interactive systems that engage users in a step-by-step dialogue to clarify their intent.\n",
      "For example, the work by Stengel-Eskin et al. (2023) introduces A MP, a framework, dataset, and challenge for translating ambiguous natural language to formal representations like logic and code, which can be used in interactive systems to handle ambiguity.\n",
      "[7, 8]\n",
      "For example, the work by Stengel-Eskin et al.\n",
      "(2023) introduces A MP, a framework, dataset, and challenge for translating ambiguous natural language to formal representations like logic and code, which can be used in interactive systems to handle ambiguity.\n",
      "Additionally, the study by Zhao et al. (2021) proposes a generation system that addresses the cold-start zero-shot clarifying question challenge in conversational search, which is another example of interactive systems that engage users in a step-by-step dialogue to clarify their intent.\n",
      "[9, 10]\n",
      "Additionally, the study by Zhao et al.\n",
      "(2021) proposes a generation system that addresses the cold-start zero-shot clarifying question challenge in conversational search, which is another example of interactive systems that engage users in a step-by-step dialogue to clarify their intent.\n",
      "Furthermore, the research by Qian et al. (2022) focuses on resolving ambiguities in text-to-image generative models through a disambiguation framework that engages users in a step-by-step dialogue to clarify their intent.\n",
      "[11, 12]\n",
      "Furthermore, the research by Qian et al.\n",
      "(2022) focuses on resolving ambiguities in text-to-image generative models through a disambiguation framework that engages users in a step-by-step dialogue to clarify their intent.\n",
      "For instance, the system proposed by Li et al. (2020) employs a parser-independent interactive approach, allowing users to refine their queries based on feedback and disambiguate potential misunderstandings.\n",
      "[13, 14]\n",
      "For instance, the system proposed by Li et al.\n",
      "(2020) employs a parser-independent interactive approach, allowing users to refine their queries based on feedback and disambiguate potential misunderstandings.\n",
      "This interactive process enhances query understanding and improves the accuracy of the generated SQL queries.\n",
      "[15]\n",
      "This interactive process enhances query understanding and improves the accuracy of the generated SQL queries.\n",
      "Another technique for addressing ambiguity is the use of disambiguation techniques within the model itself.\n",
      "[16]\n",
      "Another technique for addressing ambiguity is the use of disambiguation techniques within the model itself.\n",
      "For instance, word sense disambiguation is one of the areas in NLP that has gained significant attention and numerous works have been proposed in this regards (Wang and Wang, 2021).\n",
      "[17]\n",
      "For instance, word sense disambiguation is one of the areas in NLP that has gained significant attention and numerous works have been proposed in this regards ( Wang and Wang ,2021 ).\n",
      "Resolving ambiguities in question answering (Min et al., 2020), conversational question answering (Guo et al., 2021), and task-oriented dialogue systems (Qian et al., 2022) has also been previously studied.\n",
      "[18, 19, 20, 21]\n",
      "Resolving ambiguities in question answering ( Min et al.\n",
      ",2020 ), conversational question answering ( Guo et al.\n",
      ",2021 ), and task-oriented dialogue systems ( Qian et al.\n",
      ",2022 ) has also been previously studied.\n",
      "Ambiguity resolution has also been studied in multi-modal applications, such as multi-modal machine translation (Li et al., 2022) or matching images or videos to disambiguated interpretation of a sentence (Berzak et al., 2015).\n",
      "[22, 23, 24]\n",
      "Ambiguity resolution has also been studied in multi-modal applications, such as multi-modal machine translation ( Li et al.\n",
      ",2022 )or matching images or videos to disambiguated interpretation of a sentence ( Berzak et al.\n",
      ",2015 ).\n",
      "Despite those recent efforts, not much attention has been paid to ambiguities in text-to-image generative models.\n",
      "[25]\n",
      "Despite those recent efforts, not much attention has been paid to ambiguities in text-to-image generative models.\n",
      "On the other hand, the growing popularity of those models, both in academic and non-academic circles, make it imperatives to better understand potential issues with those systems due to language ambiguity.\n",
      "[26]\n",
      "On the other hand, the growing popularity of those models, both in academic and non-academic circles, make it imperatives to better understand potential issues with those systems due to language ambiguity.\n",
      "In this paper we have identified and addressed some of those issues.\n",
      "[27]\n",
      "In this paper we have identified and addressed some of those issues.\n",
      "We hope that our work will inspire future effort on this important problem.\n",
      "[28]\n",
      "We hope that our work will inspire future effort on this important problem.\n",
      "For example, the AmbiQT benchmark (Wang et al., 2022) introduces a dataset with ambiguous queries, each having two distinct valid SQL interpretations.\n",
      "[29]\n",
      "For example, the AmbiQT benchmark (Wang et al., 2022) introduces a dataset with ambiguous queries, each having two distinct valid SQL interpretations.\n",
      "The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation.\n",
      "[30]\n",
      "The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation.\n",
      "AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs.\n",
      "[31]\n",
      "AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs.\n",
      "Inspired by our experience with several real-world datasets, we target four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity.\n",
      "[32]\n",
      "Inspired by our experience with several real-world datasets, we target four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity.\n",
      "This benchmark encourages the development of Text2SQL models capable of handling ambiguity by considering multiple interpretations and ranking them based on their relevance to the query.\n",
      "[33]\n",
      "This benchmark encourages the development of Text2SQL models capable of handling ambiguity by considering multiple interpretations and ranking them based on their relevance to the query.\n",
      "Furthermore, the work by Pourreza and Rafiei (2023) highlights the importance of cautious interpretation of benchmark evaluations.\n",
      "[34]\n",
      "Furthermore, the work by Pourreza and Rafiei (2023) highlights the importance of cautious interpretation of benchmark evaluations.\n",
      "They demonstrate that achieving perfect performance on existing benchmarks is unfeasible due to the inherent ambiguity in natural language queries.\n",
      "[35]\n",
      "They demonstrate that achieving perfect performance on existing benchmarks is unfeasible due to the inherent ambiguity in natural language queries.\n",
      "Their evaluation reveals that the true performance of Text2SQL models may be underestimated, emphasizing the need for additional independent evaluations and the consideration of multiple valid interpretations in benchmark design.\n",
      "[36]\n",
      "Their evaluation reveals that the true performance of Text2SQL models may be underestimated, emphasizing the need for additional independent evaluations and the consideration of multiple valid interpretations in benchmark design.\n",
      "In conclusion, addressing ambiguity in Text2SQL remains an active area of research.\n",
      "[37]\n",
      "In conclusion, addressing ambiguity in Text2SQL remains an active area of research.\n",
      "Ambiguity in SQL has been studied in other fields of NLP, but it has been unexplored in the context of semantic parsing.\n",
      "[38]\n",
      "Ambiguity in SQL has been studied in other fields of NLP, but it has been unexplored in the context of semantic parsing.\n",
      "AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.\n",
      "[39]\n",
      "AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.\n",
      "Interactive systems, disambiguation techniques, and careful interpretation of benchmark evaluations are essential for developing accurate and robust Text2SQL models capable of handling the complexities of natural language queries.\n",
      "[40]\n",
      "Interactive systems, disambiguation techniques, and careful interpretation of benchmark evaluations are essential for developing accurate and robust Text2SQL models capable of handling the complexities of natural language queries.\n",
      "The ethical implications of Text2SQL technology cannot be overlooked, especially considering its potential applications in sensitive domains like healthcare, finance, and government.\n",
      "[0]\n",
      "\n",
      "## 2.6 Ethical Considerations\n",
      "\n",
      "The ethical implications of Text2SQL technology cannot be overlooked, especially considering its potential applications in sensitive domains like healthcare, finance, and government.\n",
      "While Text2SQL systems offer immense benefits by democratizing access to data, they also raise concerns regarding data privacy, fairness, and potential biases inherited from training data.\n",
      "[1]\n",
      "While Text2SQL systems offer immense benefits by democratizing access to data, they also raise concerns regarding data privacy, fairness, and potential biases inherited from training data.\n",
      "For instance, systems trained on large-scale unfiltered data can suffer from degenerated and biased behavior, which can reflect and reinforce societal biases and structural inequalities.\n",
      "[2]\n",
      "For instance, systems trained on large-scale unfiltered data can suffer from degenerated and biased behavior, which can reflect and reinforce societal biases and structural inequalities.\n",
      "Additionally, the risks associated with neural rendering studies, such as privacy and security issues linked to the capture of sensitive information, are also relevant to Text2SQL systems.\n",
      "[3]\n",
      "Additionally, the risks associated with neural rendering studies, such as privacy and security issues linked to the capture of sensitive information, are also relevant to Text2SQL systems.\n",
      "Recent studies, such as the work by Liu et al. (2023), have uncovered social biases in Text2SQL models, highlighting the need for careful consideration of the potential consequences of deploying these systems in real-world applications.\n",
      "[6]\n",
      "(2023), have uncovered social biases in Text2SQL models, highlighting the need for careful consideration of the potential consequences of deploying these systems in real-world applications.\n",
      "Text-to-SQL models bridge the gap between database manipulation and amateur users and are mainly applied by administrative industries, such as banks, schools, and governments, which rely on AI-based applications to manipulate databases and further develop policies that will have profound impacts on various aspects of many people’s lives.\n",
      "[7]\n",
      "Text-to-SQL models bridge the gap between database manipulation and amateur users and are mainly applied by administrative industries, such as banks, schools, and governments, which rely on AI-based applications to manipulate databases and further develop policies that will have profound impacts on various aspects of many people’s lives.\n",
      "Unfortunately, large pre-trained language models (PLMs) are actually acknowledged to contain social biases towards different demographics, and these wicked biases are observed to be inherited by downstream tasks.\n",
      "[9]\n",
      "Unfortunately, large pre-trained language models (PLMs) are actually acknowledged to contain social biases towards different demographics, and these wicked biases are observed to be inherited by downstream tasks.\n",
      "However, as we observed through experiments, social biases are integrally inherited by downstream models even fine-tuned on neutral data, as in the Text-to-SQL task.\n",
      "[11]\n",
      "However, as we observed through experiments, social biases are integrally inherited by downstream models even fine-tuned on neutral data, as in the Text-to-SQL task.\n",
      "These biases can manifest in various forms, including stereotypical correlations between judgmental expressions and different demographics, as well as incorrect comparisons that perpetuate harmful stereotypes.\n",
      "[12]\n",
      "These biases can manifest in various forms, including stereotypical correlations between judgmental expressions and different demographics, as well as incorrect comparisons that perpetuate harmful stereotypes.\n",
      "The words for Middle-Eastern and Asian personas connect to critiques of Orientalism, a damaging depiction where the East (encompassing Asia and the Middle East) is represented as the “ultimate Other” against which Western culture is defined; inaccurate, romanticized representations of these cultures have historically been used as implicit justification for imperialism in these areas (Said, 1978; Ma, 2000; Yoshihara, 2002).\n",
      "[21]\n",
      "The words for Middle-Eastern and Asian personas connect to critiques of Orientalism, a damaging depiction where the East (encompassing Asia and the Middle East) is represented as the “ultimate Other” against which Western culture is defined; inaccurate, romanticized representations of these cultures have historically been used as implicit justification for imperialism in these areas (Said, 1978; Ma, 2000; Yoshihara, 2002).\n",
      "This reflects essentialism: individuals in these groups are defined solely by a limited, seemingly-fixed essential set of characteristics rather than their full humanity (Rosenblum and Travis, 1996; Woodward, 1997).\n",
      "[23]\n",
      "This reflects essentialism: individuals in these groups are defined solely by a limited, seemingly-fixed essential set of characteristics rather than their full humanity (Rosenblum and Travis, 1996; Woodward, 1997).\n",
      "To address these concerns, researchers have proposed several approaches. The BiaSpider benchmark (Liu et al., 2023) aims to uncover and categorize social biases in Text2SQL models by introducing a new paradigm for structured data bias measurement.\n",
      "[25, 26]\n",
      "To address these concerns, researchers have proposed several approaches.\n",
      "The BiaSpider benchmark (Liu et al., 2023) aims to uncover and categorize social biases in Text2SQL models by introducing a new paradigm for structured data bias measurement  .\n",
      "Additionally, the work by Awasthi et al. (2023) emphasizes the importance of reviewing Text2SQL systems for harmful biases before deployment and ensuring that users are aware of the potential for incorrect answers.\n",
      "[28, 29]\n",
      "Additionally, the work by Awasthi et al.\n",
      "(2023) emphasizes the importance of reviewing Text2SQL systems for harmful biases before deployment and ensuring that users are aware of the potential for incorrect answers .\n",
      "This highlights the need for responsible development and deployment of Text2SQL technology, with a focus on fairness, transparency, and accountability.\n",
      "[30]\n",
      "This highlights the need for responsible development and deployment of Text2SQL technology, with a focus on fairness, transparency, and accountability.\n",
      "The evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al., 2018 ), SPIDER ( Yu et al., 2018 ), KaggleDBQA ( Lee et al., 2021 ), SEDE ( Hazoom et al., 2021 ), and EHRSQL ( Lee et al., 2022 ).\n",
      "[0, 1, 2, 3, 4, 5]\n",
      "\n",
      "## 3.1 Benchmarks\n",
      "\n",
      "The evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al.\n",
      ",2018 ), SPIDER ( Yu et al.\n",
      ",2018 ), KaggleDBQA ( Lee et al.\n",
      ",2021 ), SEDE ( Hazoom et al.\n",
      ",2021 ), and EHRSQL ( Lee et al.\n",
      ",2022 ).\n",
      "These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets.\n",
      "[6]\n",
      "These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets.\n",
      "Dr. SPIDER ( Chang et al., 2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER.\n",
      "[7, 8]\n",
      "Dr. SPIDER ( Chang et al.\n",
      ",2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER.\n",
      "Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges.\n",
      "[9]\n",
      "Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges.\n",
      "For instance, WikiSQL ( Zhong et al., 2018 ) and SPIDER ( Yu et al., 2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al., 2021 ), SEDE ( Hazoom et al., 2021 ), and EHRSQL ( Lee et al., 2022 ) aim to capture real-world scenarios.\n",
      "[10, 11, 12, 13, 14, 15]\n",
      "For instance, WikiSQL ( Zhong et al.\n",
      ",2018 )and SPIDER ( Yu et al.\n",
      ",2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al.\n",
      ",2021 ), SEDE ( Hazoom et al.\n",
      ",2021 ), and EHRSQL ( Lee et al.\n",
      ",2022 ) aim to capture real-world scenarios.\n",
      "The AmbiQT benchmark ( Wang et al., 2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.\n",
      "[18, 19]\n",
      "The AmbiQT benchmark ( Wang et al.\n",
      ",2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion.\n",
      "Additionally, Text2Analysis ( He et al., 2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.\n",
      "[20, 21]\n",
      "Additionally, Text2Analysis ( He et al.\n",
      ",2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.\n",
      "These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.\n",
      "[22]\n",
      "These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.\n",
      "One of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database.\n",
      "[23]\n",
      "One of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database.\n",
      "WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models.\n",
      "[24]\n",
      "WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models.\n",
      "However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios.\n",
      "[25]\n",
      "However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios.\n",
      "For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35%!\n",
      "[26]\n",
      "For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35% .\n",
      "Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios.\n",
      "[27]\n",
      "Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios .\n",
      "To address this limitation, the Spider benchmark (Yu et al., 2018) was introduced.\n",
      "[28]\n",
      "To address this limitation, the Spider benchmark (Yu et al., 2018) was introduced.\n",
      "Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas.\n",
      "[29]\n",
      "Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas.\n",
      "Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.\n",
      "[30]\n",
      "Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.\n",
      "Another notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context.\n",
      "[31]\n",
      "Another notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context.\n",
      "This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical.\n",
      "[32, 33, 34]\n",
      "This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical.\n",
      "Each domain contains millions of passages and sufficient human-annotated query-passage related pairs, collected from real search engine systems within Alibaba Group.\n",
      "The authenticity of the samples allows SParC to meet the needs of both academia and industry fields, pushing forward the quality and variety of Chinese passage retrieval datasets.\n",
      "SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions.\n",
      "[35]\n",
      "SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions.\n",
      "This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.\n",
      "[36]\n",
      "This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.\n",
      "AmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL.\n",
      "[37]\n",
      "AmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL.\n",
      "AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.\n",
      "[38, 39, 40, 41, 42]\n",
      "AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.\n",
      "The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation.\n",
      "It consists of natural language questions with multiple valid SQL interpretations, each representing a different interpretation of the user\\'s intent.\n",
      "AmbiQT challenges Text2SQL models to handle ambiguity and rank multiple interpretations based on their relevance to the query.\n",
      "AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, targeting four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity.\n",
      "When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution.\n",
      "[43]\n",
      "When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution.\n",
      "We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling.\n",
      "[44, 45, 46]\n",
      "We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling.\n",
      "Most outputs are small lexical tweaks of the top choice, bringing about little meaningful diversity in SQL structures or schema alternatives.\n",
      "Even SOTA LLMs like ChatGPT suffer from this issue.\n",
      "WikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions.\n",
      "[47]\n",
      "WikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions.\n",
      "The landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning.\n",
      "[54]\n",
      "### 3.2 Models\n",
      "\n",
      "The landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning.\n",
      "One of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model.\n",
      "[56]\n",
      "**Sequence-to-Sequence Models:**\n",
      "\n",
      "One of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model.\n",
      "This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries.\n",
      "[57]\n",
      "This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries.\n",
      "Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a).\n",
      "[59]\n",
      "Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a).\n",
      "Graph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas.\n",
      "[61]\n",
      "**Graph-Based Models:**\n",
      "\n",
      "Graph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas.\n",
      "These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation.\n",
      "[62]\n",
      "These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation.\n",
      "Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema.\n",
      "[63]\n",
      "Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema.\n",
      "Hybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths.\n",
      "[65]\n",
      "**Hybrid Models:**\n",
      "\n",
      "Hybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths.\n",
      "An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.\n",
      "[67]\n",
      "An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.\n",
      "The integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness.\n",
      "[68]\n",
      "**Large Language Models (LLMs):**\n",
      "\n",
      "The integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness.\n",
      "Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy.\n",
      "[69]\n",
      "Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy.\n",
      "Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.\n",
      "[70]\n",
      "Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.\n",
      "Interactive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries.\n",
      "[71]\n",
      "**Interactive Systems:**\n",
      "\n",
      "Interactive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries.\n",
      "These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings.\n",
      "[72]\n",
      "These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings.\n",
      "An example of an interactive system is the one proposed by Li et al. (2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.\n",
      "[73, 74]\n",
      "Examples of interactive systems include the one proposed by Li et al.\n",
      "(2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.\n",
      "This is evident in the Text2Analysis benchmark, which addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis, providing a comprehensive taxonomy of advanced analysis and unclear queries, which enables the evaluation of the analytical abilities of large language models.\n",
      "[1]\n",
      "This is evident in the Text2Analysis benchmark, which addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis, providing a comprehensive taxonomy of advanced analysis and unclear queries, which enables the evaluation of the analytical abilities of large language models.\n",
      "Additionally, the evaluation of five state-of-the-art models on the Text2Analysis dataset reveals their strengths and weaknesses in handling advanced analysis tasks and unclear queries, providing valuable insights for future research.\n",
      "[2]\n",
      "Additionally, the evaluation of five state-of-the-art models on the Text2Analysis dataset reveals their strengths and weaknesses in handling advanced analysis tasks and unclear queries, providing valuable insights for future research.\n",
      "However, the current evaluation metrics have limitations that need to be addressed to ensure a comprehensive and accurate assessment of model capabilities.\n",
      "[3]\n",
      "However, the current evaluation metrics have limitations that need to be addressed to ensure a comprehensive and accurate assessment of model capabilities.\n",
      "For instance, comparisons are limited to publicly available checkpoints, which can lead to significant confounding variables due to differences in training recipes and datasets.\n",
      "[4]\n",
      "For instance, comparisons are limited to publicly available checkpoints, which can lead to significant confounding variables due to differences in training recipes and datasets.\n",
      "Additionally, the focus on specific aspects of 3D awareness, such as single-image surface reconstruction and multiview consistency, may not provide a comprehensive understanding of a model's 3D capabilities.\n",
      "[5]\n",
      "Additionally, the focus on specific aspects of 3D awareness, such as single-image surface reconstruction and multiview consistency, may not provide a comprehensive understanding of a model's 3D capabilities.\n",
      "Furthermore, the reliance on probing methods like linear probes and zero-shot analysis may not fully capture the model's ability to adapt to 3D tasks.\n",
      "[6]\n",
      "Furthermore, the reliance on probing methods like linear probes and zero-shot analysis may not fully capture the model's ability to adapt to 3D tasks.\n",
      "This limitation can lead to underestimating the true performance of Text2SQL models, as demonstrated by Pourreza and Rafiei (2023).\n",
      "[11]\n",
      "This limitation can lead to underestimating the true performance of Text2SQL models, as demonstrated by Pourreza and Rafiei (2023).\n",
      "It assumes that the reference queries are error-free and may not account for alternative valid queries that could also produce correct results.\n",
      "[14]\n",
      "It assumes that the reference queries are error-free and may not account for alternative valid queries that could also produce correct results.\n",
      "Additionally, execution accuracy can be affected by ties in the database, where multiple rows satisfy the query conditions, leading to potential discrepancies in the evaluation results.\n",
      "[15]\n",
      "Additionally, execution accuracy can be affected by ties in the database, where multiple rows satisfy the query conditions, leading to potential discrepancies in the evaluation results.\n",
      "Semantic entropy improves over baselines in predicting whether a model’s answer to a question is correct.\n",
      "[24]\n",
      "Semantic entropy improves over baselines in predicting whether a model’s answer to a question is correct.\n",
      "This can be achieved by leveraging techniques like query rewriting and normalization to identify semantically equivalent queries.\n",
      "[25]\n",
      "This can be achieved by leveraging techniques like query rewriting and normalization to identify semantically equivalent queries.\n",
      "Query rewriting aims to train a rewriting model to mimic human-rewritten queries, which can solve ambiguous problems and recover missing elements from the context.\n",
      "[26]\n",
      "Query rewriting aims to train a rewriting model to mimic human-rewritten queries, which can solve ambiguous problems and recover missing elements from the context.\n",
      "Query expansion methods, such as selecting terms via the normalization score of their embeddings, can also enhance search queries and produce better retrieval results.\n",
      "[27]\n",
      "Query expansion methods, such as selecting terms via the normalization score of their embeddings, can also enhance search queries and produce better retrieval results.\n",
      "Integrating both query rewriting and query expansion can reformulate better conversational queries.\n",
      "[28]\n",
      "Integrating both query rewriting and query expansion can reformulate better conversational queries.\n",
      "For instance, the AmbigQA dataset measures a model’s ability to disambiguate-and-answer ambiguous questions, such as determining the specific game in the 'Fallout' series being referred to in a query like “Where does the new fallout game take place?” and then providing the correct location, “Appalachia”.\n",
      "[30]\n",
      "For instance, the AmbigQA dataset measures a model’s ability to disambiguate-and-answer ambiguous questions, such as determining the specific game in the 'Fallout' series being referred to in a query like “Where does the new fallout game take place?” and then providing the correct location, “Appalachia”.\n",
      "Furthermore, SituatedQA focuses on temporal and geographic ambiguity, where additional time ranges and their corresponding answers are crowdsourced, and geographic questions are created by removing references to location and then crowdsourcing locations and corresponding answers.\n",
      "[31]\n",
      "Furthermore, SituatedQA focuses on temporal and geographic ambiguity, where additional time ranges and their corresponding answers are crowdsourced, and geographic questions are created by removing references to location and then crowdsourcing locations and corresponding answers.\n",
      "These datasets demonstrate the importance of accounting for ambiguity in natural language questions to improve model performance and calibration.\n",
      "[32]\n",
      "These datasets demonstrate the importance of accounting for ambiguity in natural language questions to improve model performance and calibration.\n",
      "For example, in the context of multimodal fusion, it has been observed that increased data diversity can lead to substantial improvements in performance, especially in scarce data regimes.\n",
      "[34]\n",
      "For example, in the context of multimodal fusion, it has been observed that increased data diversity can lead to substantial improvements in performance, especially in scarce data regimes.\n",
      "Furthermore, fine-grained evaluation tests can be designed to assess specific model capabilities, such as understanding of ontology, logical equivalence, and answering under visual obfuscation.\n",
      "[35]\n",
      "Furthermore, fine-grained evaluation tests can be designed to assess specific model capabilities, such as understanding of ontology, logical equivalence, and answering under visual obfuscation.\n",
      "For instance, denotation accuracy, widely used in semantic parsing, is not directly applicable to tasks where tabular input encoding, reasoning, and generation are performed by the same model.\n",
      "[37]\n",
      "For instance, denotation accuracy, widely used in semantic parsing, is not directly applicable to tasks where tabular input encoding, reasoning, and generation are performed by the same model.\n",
      "Additionally, the strict binary measure of table exact match may not be ideal for queries that do not impose ordering among columns or rows.\n",
      "[38]\n",
      "Additionally, the strict binary measure of table exact match may not be ideal for queries that do not impose ordering among columns or rows.\n",
      "Furthermore, the limitations in training and benchmarking, as well as the need for more diverse and larger human evaluation, highlight the importance of exploring more evaluation approaches and metrics.\n",
      "[39]\n",
      "Furthermore, the limitations in training and benchmarking, as well as the need for more diverse and larger human evaluation, highlight the importance of exploring more evaluation approaches and metrics.\n",
      "Training limitations include the constraint on model architecture and size, which may be improved by exploring specific architecture enhancements or larger models.\n",
      "[43]\n",
      "Training limitations include the constraint on model architecture and size, which may be improved by exploring specific architecture enhancements or larger models.\n",
      "Benchmark limitations involve the scope of methods included and the calibration of dataset protocols, suggesting a need for a wider method spectrum and further work on aspects like the impact of the number of input frames.\n",
      "[44]\n",
      "Benchmark limitations involve the scope of methods included and the calibration of dataset protocols, suggesting a need for a wider method spectrum and further work on aspects like the impact of the number of input frames.\n",
      "Evaluation limitations highlight the need for a more diverse and larger pool of participants in human evaluations, as well as the exploration of additional evaluation approaches and metrics for a more holistic assessment of models.\n",
      "[45]\n",
      "Evaluation limitations highlight the need for a more diverse and larger pool of participants in human evaluations, as well as the exploration of additional evaluation approaches and metrics for a more holistic assessment of models.\n",
      "These insights are drawn from studies that have examined the prevalent methods, representative datasets, and powerful benchmarks in the field, acknowledging that while progress has been made, there is still much to be done to refine evaluation metrics.\n",
      "[46]\n",
      "These insights are drawn from studies that have examined the prevalent methods, representative datasets, and powerful benchmarks in the field, acknowledging that while progress has been made, there is still much to be done to refine evaluation metrics.\n",
      "Combining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases.\n",
      "[0]\n",
      "\n",
      "## 4.2 Combining Text2SQL with Other Tasks\n",
      "\n",
      "Combining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases.\n",
      "By integrating Text2SQL with tasks like question answering, information extraction, and natural language generation, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.\n",
      "[1]\n",
      "By integrating Text2SQL with tasks like question answering, information extraction, and natural language generation, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.\n",
      "By combining Text2SQL with QA, we can build systems that can not only translate natural language questions into SQL queries but also answer those questions directly using the retrieved data.\n",
      "[3]\n",
      "By combining Text2SQL with QA, we can build systems that can not only translate natural language questions into SQL queries but also answer those questions directly using the retrieved data.\n",
      "This integration can be achieved by incorporating QA models into the Text2SQL pipeline, allowing the system to generate natural language answers based on the results of the executed SQL queries.\n",
      "[4]\n",
      "This integration can be achieved by incorporating QA models into the Text2SQL pipeline, allowing the system to generate natural language answers based on the results of the executed SQL queries.\n",
      "By combining Text2SQL with IE, we can build systems that can extract structured information from unstructured text sources and store it in databases.\n",
      "[7]\n",
      "By combining Text2SQL with IE, we can build systems that can extract structured information from unstructured text sources and store it in databases.\n",
      "For instance, UniEX: an Effective and Efficient Framework for Unified Information Extraction Via a Span-extractive Perspective demonstrates the potential of using a unified extractive framework for various IE tasks, which can be beneficial for the Text2SQL pipeline.\n",
      "[9]\n",
      "For instance, UniEX: an Effective and Efficient Framework for Unified Information Extraction Via a Span-extractive Perspective () demonstrates the potential of using a unified extractive framework for various IE tasks, which can be beneficial for the Text2SQL pipeline.\n",
      "Additionally, the work on Benchmarking and Improving Text-to-SQL Generation under Ambiguity highlights the importance of addressing ambiguity in SQL generation, a critical aspect when integrating IE models into the Text2SQL process.\n",
      "[10]\n",
      "Additionally, the work on Benchmarking and Improving Text-to-SQL Generation under Ambiguity () highlights the importance of addressing ambiguity in SQL generation, a critical aspect when integrating IE models into the Text2SQL process.\n",
      "Integrating Text2SQL with natural language generation (NLG) tasks can enable the generation of natural language explanations and summaries of query results.\n",
      "[12]\n",
      "Furthermore, integrating Text2SQL with natural language generation (NLG) tasks can enable the generation of natural language explanations and summaries of query results.\n",
      "For example, the system proposed by Kokkalis et al. (2012) translates SQL queries into narratives, providing users with a more intuitive understanding of the query results.\n",
      "[15]\n",
      "(2012) translates SQL queries into narratives, providing users with a more intuitive understanding of the query results.\n",
      "By integrating Text2SQL with QA, IE, and NLG tasks, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.\n",
      "[17]\n",
      "By integrating Text2SQL with QA, IE, and NLG tasks, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.\n",
      "Addressing bias in Text2SQL systems is of paramount importance, especially considering their potential applications in sensitive domains like healthcare, finance, and government.\n",
      "[0]\n",
      "\n",
      "## 4.3 Addressing Bias\n",
      "\n",
      "Addressing bias in Text2SQL systems is of paramount importance, especially considering their potential applications in sensitive domains like healthcare, finance, and government.\n",
      "Biased Text2SQL models can perpetuate and amplify existing stereotypes, leading to unfair and discriminatory outcomes.\n",
      "[1]\n",
      "Biased Text2SQL models can perpetuate and amplify existing stereotypes, leading to unfair and discriminatory outcomes.\n",
      "One approach to addressing bias is through the use of diverse and representative training data.\n",
      "[3]\n",
      "One approach to addressing bias is through the use of diverse and representative training data.\n",
      "Techniques like data augmentation and synthetic data generation can be employed to create more diverse training datasets and improve the generalizability of Text2SQL models.\n",
      "[5]\n",
      "Techniques like data augmentation and synthetic data generation can be employed to create more diverse training datasets and improve the generalizability of Text2SQL models.\n",
      "Another important strategy is to incorporate bias mitigation techniques during the model development process.\n",
      "[6]\n",
      "Another important strategy is to incorporate bias mitigation techniques during the model development process.\n",
      "This can involve using techniques like adversarial training, which aims to minimize the model's reliance on biased features, or incorporating fairness constraints into the training objective.\n",
      "[7]\n",
      "This can involve using techniques like adversarial training, which aims to minimize the model's reliance on biased features, or incorporating fairness constraints into the training objective.\n",
      "These techniques can help ensure that the model treats different demographic groups fairly and avoids perpetuating harmful stereotypes.\n",
      "[8]\n",
      "These techniques can help ensure that the model treats different demographic groups fairly and avoids perpetuating harmful stereotypes.\n",
      "Furthermore, it is crucial to evaluate Text2SQL models for bias and fairness before deployment.\n",
      "[9]\n",
      "Furthermore, it is crucial to evaluate Text2SQL models for bias and fairness before deployment.\n",
      "By carefully evaluating and addressing bias, we can ensure that Text2SQL systems are fair, transparent, and accountable.\n",
      "[11]\n",
      "By carefully evaluating and addressing bias, we can ensure that Text2SQL systems are fair, transparent, and accountable.\n",
      "By incorporating diverse training data, bias mitigation techniques, and rigorous evaluation procedures, we can develop Text2SQL models that are not only accurate and efficient but also ethical and trustworthy.\n",
      "[13]\n",
      "By incorporating diverse training data, bias mitigation techniques, and rigorous evaluation procedures, we can develop Text2SQL models that are not only accurate and efficient but also ethical and trustworthy.\n",
      "The field of Text2SQL is rapidly evolving, with numerous opportunities for future research and development.\n",
      "[0]\n",
      "\n",
      "## 4.4 Future Research Directions\n",
      "\n",
      "The field of Text2SQL is rapidly evolving, with numerous opportunities for future research and development.\n",
      "Integrating more advanced NLP techniques into Text2SQL models can significantly enhance their understanding of natural language and improve query generation accuracy.\n",
      "[2]\n",
      "**Advanced NLP Techniques:**\n",
      "\n",
      "Integrating more advanced NLP techniques into Text2SQL models can significantly enhance their understanding of natural language and improve query generation accuracy.\n",
      "For instance, the use of chain-of-thought prompting has been shown to improve performance on text-to-SQL parsing tasks, as demonstrated by the question-decomposition prompting method (QDecomp) which outperforms existing prompting methods by 2.4 and 1.5 point absolute gains on the development set of Spider and Spider Realistic datasets.\n",
      "[3]\n",
      "For instance, the use of chain-of-thought prompting has been shown to improve performance on text-to-SQL parsing tasks, as demonstrated by the question-decomposition prompting method (QDecomp) which outperforms existing prompting methods by 2.4 and 1.5 point absolute gains on the development set of Spider and Spider Realistic datasets.\n",
      "Combining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems.\n",
      "[6]\n",
      "**Combining Text2SQL with Other Tasks:**\n",
      "\n",
      "Combining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems.\n",
      "Ambiguity in SQL, arising from related column names, has been studied in (Wang et al., 2022), but they only consider column ambiguity.\n",
      "[11]\n",
      "Ambiguity in SQL, arising from related column names, has been studied in (Wang et al., 2022), but they only consider column ambiguity.\n",
      "To the best of our discernment, AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives.\n",
      "[13]\n",
      "To the best of our discernment, AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives.\n",
      "Exploring techniques like interactive systems, disambiguation methods, and domain adaptation can help models better handle these challenges and improve their performance in real-world scenarios.\n",
      "[17]\n",
      "Exploring techniques like interactive systems, disambiguation methods, and domain adaptation can help models better handle these challenges and improve their performance in real-world scenarios.\n",
      "Additionally, investigating the use of transfer learning and few-shot learning can enable models to quickly adapt to new domains and tasks with limited training data.\n",
      "[21]\n",
      "Additionally, investigating the use of transfer learning and few-shot learning can enable models to quickly adapt to new domains and tasks with limited training data.\n",
      "Continuing to address ethical considerations and bias mitigation in Text2SQL systems is essential for building fair and responsible natural language interfaces to databases.\n",
      "[26]\n",
      "**Ethical Considerations and Bias Mitigation:**\n",
      "\n",
      "Continuing to address ethical considerations and bias mitigation in Text2SQL systems is essential for building fair and responsible natural language interfaces to databases.\n",
      "The Text2SQL task has seen significant advancements in recent years, driven by the integration of deep learning, large language models, and interactive systems.\n",
      "[0]\n",
      "\n",
      "## 5 Conclusion\n",
      "\n",
      "The Text2SQL task has seen significant advancements in recent years, driven by the integration of deep learning, large language models, and interactive systems.\n",
      "This research survey has provided a comprehensive overview of the current state of Text2SQL technology, exploring its evolution, key benchmarks and models, limitations, and future directions.\n",
      "[1]\n",
      "This research survey has provided a comprehensive overview of the current state of Text2SQL technology, exploring its evolution, key benchmarks and models, limitations, and future directions.\n",
      "The Text2Analysis benchmark is proposed as a new benchmark to further explore LLMs’ upper limits in challenging tabular data analysis tasks.\n",
      "[2]\n",
      "The Text2Analysis benchmark is proposed as a new benchmark to further explore LLMs’ upper limits in challenging tabular data analysis tasks .\n",
      "We have presented the Text2Analysis dataset that addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis.\n",
      "[3]\n",
      "We have presented the Text2Analysis dataset that addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis .\n",
      "A Text-to-SQL model takes as input a question expressed as a natural language text x, and a database schema comprising of table and column names, and outputs an SQL program y which can be executed against the database to answer the user’s question.\n",
      "[4]\n",
      "A Text-to-SQL model takes as input a question expressed as a natural language text x, and a database schema comprising of table and column names, and outputs an SQL program y which can be executed against the database to answer the user’s question .\n",
      "In this paper, we propose to uncover and categorize social biases in the Text-to-SQL task.\n",
      "[5]\n",
      "In this paper, we propose to uncover and categorize social biases in the Text-to-SQL task .\n",
      "The survey began by discussing the background and related work, highlighting the early developments in Text2SQL, the impact of deep learning and large language models, and techniques for data augmentation and ambiguity handling.\n",
      "[6]\n",
      "The survey began by discussing the background and related work, highlighting the early developments in Text2SQL, the impact of deep learning and large language models, and techniques for data augmentation and ambiguity handling.\n",
      "It then delved into the current benchmarks and models, analyzing popular benchmarks like WikiSQL and Spider, and examining different Text2SQL models, including sequence-to-sequence models, graph-based models, and hybrid models.\n",
      "[7]\n",
      "It then delved into the current benchmarks and models, analyzing popular benchmarks like WikiSQL and Spider, and examining different Text2SQL models, including sequence-to-sequence models, graph-based models, and hybrid models.\n",
      "The survey continued by discussing the limitations of current Text2SQL systems and proposing potential solutions and future research directions.\n",
      "[8]\n",
      "The survey continued by discussing the limitations of current Text2SQL systems and proposing potential solutions and future research directions.\n",
      "This included a critical analysis of evaluation metrics, the potential for combining Text2SQL with other NLP tasks, methods for addressing bias, and new research directions for advancing Text2SQL technology.\n",
      "[9]\n",
      "This included a critical analysis of evaluation metrics, the potential for combining Text2SQL with other NLP tasks, methods for addressing bias, and new research directions for advancing Text2SQL technology.\n",
      "The survey concludes by summarizing the key findings and highlighting the potential impact of Text2SQL technology.\n",
      "[10]\n",
      "The survey concludes by summarizing the key findings and highlighting the potential impact of Text2SQL technology.\n",
      "The integration of neural networks, LLMs, and interactive systems has revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.\n",
      "[11]\n",
      "The integration of neural networks, LLMs, and interactive systems has revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.\n",
      "However, challenges remain in addressing ambiguity, bias, and real-world complexities.\n",
      "[12]\n",
      "However, challenges remain in addressing ambiguity, bias, and real-world complexities.\n",
      "For instance, preferences and values are not universal, and they are often inconsistently defined.\n",
      "[13]\n",
      "For instance, preferences and values are not universal, and they are often inconsistently defined .\n",
      "Additionally, human feedback is inherently incomplete, and operationalizing a 'good' output is difficult.\n",
      "[14]\n",
      "Additionally, human feedback is inherently incomplete, and operationalizing a 'good' output is difficult .\n",
      "Furthermore, crowdworkers and social media users are neither representative nor sufficient, which can lead to biased outcomes.\n",
      "[15]\n",
      "Furthermore, crowdworkers and social media users are neither representative nor sufficient, which can lead to biased outcomes .\n",
      "Future research directions include exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations.\n",
      "[16]\n",
      "Future research directions include exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations.\n",
      "By continuing to advance Text2SQL technology, we can unlock its full potential for empowering users to access and analyze data more effectively.\n",
      "[17]\n",
      "By continuing to advance Text2SQL technology, we can unlock its full potential for empowering users to access and analyze data more effectively.\n"
     ]
    }
   ],
   "source": [
    "for result,new_section in zip(results,new_sections):\n",
    "    for r in result:\n",
    "        print(r[\"statement\"])\n",
    "        print(r[\"related_sen_id\"])\n",
    "        # a = re.findall(r\"{}\".format(r[\"statement\"]),new_section)\n",
    "        sentences = sent_tokenize(new_section)\n",
    "        for id in r[\"related_sen_id\"]:\n",
    "            print(sentences[id])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "## 1 Introduction\n",
      "\n",
      "Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query. However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors. For example, a query asking for the capacity of O2 Arena could be ambiguous if the schema has separate columns for standing and seating capacity. Similarly, a query on the number of under-nourished children is ambiguous if there are different columns for 'under-weight children' and 'stunted growth in children'. This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers. This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent. To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity. This benchmark aims to test the performance of Text-to-SQL systems under ambiguity and encourage the development of more robust and accurate models.\n",
      "\n",
      "In this survey, we explore the current state of Text-to-SQL technology, focusing on the challenges posed by ambiguity and the approaches used to address it. We discuss the limitations of existing benchmarks and evaluation metrics, and propose potential improvements to ensure a more comprehensive and accurate assessment of model capabilities. Benchmarks like PredBench [PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines, 66908e2301d2a3fbfcea14d7, 8] have limitations in terms of training, benchmarking, and evaluation. For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols. Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics. To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance. Additionally, incorporating indicators of attack failures [Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples, 60d140d191e011c16f0cb388, 5] could help in debugging faulty evaluations and lead to fairer assessments. Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments [STEER: Assessing the Economic Rationality of Large Language Models, 65cec1c1939a5f40828f00d7, 11] to evaluate models' ability to exhibit rational behavior in economic tasks. We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems. For example, the AmbiQT benchmark (Benchmarking and Improving Text-to-SQL Generation under Ambiguity, 6535d747939a5f408295c649, 1) addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates. Furthermore, the work on interactive Text-to-SQL generation (Interactive Text-to-SQL Generation Via Editable Step-by-Step Explanations, 6461b9c9d68f896efad43133, 1) proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation. Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL (Exploring Chain-of-Thought Style Prompting for Text-to-SQL, 646d8642d68f896efa0a3040, 1) aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task. Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness. Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.\n",
      "\n",
      "## 2.1 Early Developments\n",
      "\n",
      "The early approaches to Text2SQL can be categorized into rule-based systems and grammar-based methods. Rule-based systems, such as the one proposed by Hendrix et al. (1978), relied on handcrafted rules to map natural language questions to SQL queries. For example, PGTune [1] makes configuration recommendations by asking users for basic information about the Postgres database they are using and the details about their hardware environment. Note that the information of the Postgres’ version and the number of CPUs affects the setting of the knobs because a new version will introduce new knobs. For versions below 9.5, max_worker_processes is not available. Similarly, max_parallel_workers_per_gather supports versions higher than 9.5, and max_parallel_workers supports v10 and higher versions. The setting of the knob values also follows the rules. These rules include that the values of max_worker_processes and max_parallel_workers are equal to the number of CPUs and the value of max_parallel_workers_per_gather is half the number of CPUs. These systems were limited in their ability to handle complex queries and required extensive manual effort to create and maintain the rules. Grammar-based methods, like the one developed by Giordani and Moschitti (2012), used generative parsers to translate questions into SQL queries. While these methods offered some flexibility, they still struggled with the inherent complexity and ambiguity of natural language. For instance, the approach necessitates meticulous prompt design to generate exercises, which inevitably entails human intervention, introducing potential bias or variability and may not scale efficiently. Additionally, the quality and correctness of the generated problems are not explicitly addressed, and the current framework relies on a source problem for exercise generation, limiting flexibility and robustness. Furthermore, the handling of ambiguity in natural language is a significant challenge, as models often fail to capture the distribution of possible meanings without deliberate instruction.\n",
      "\n",
      "## 2.2 Deep Learning Era\n",
      "\n",
      "The advent of deep learning brought about a paradigm shift in the field of Text2SQL, enabling the construction of several large text-to-SQL datasets, such as WikiSQL (Zhong et al., 2017) and Spider (Yu et al., 2018), and achieving unprecedented performance in recent years (Rubin and Berant, 2021; Wang et al., 2020a; Scholak et al., 2021; Yu et al., 2020; Hwang et al., 2019). Neural network-based models, particularly sequence-to-sequence models, demonstrated remarkable improvements in translation accuracy and generalization capabilities. Notable examples include Seq2SQL (Zhong et al., 2017), which employed reinforcement learning to generate SQL queries, and RATSQL (Wang et al., 2020a), which introduced a relation-aware self-attention mechanism to better encode the relationships between columns and tables. These models leveraged the power of deep learning to capture the complexities of natural language and database schemas, leading to more accurate and robust Text2SQL systems.\n",
      "\n",
      "Furthermore, the integration of large language models (LLMs) like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) into Text2SQL further pushed the boundaries of performance. These pre-trained models, fine-tuned on Text2SQL tasks, demonstrated superior understanding of language semantics and context, resulting in more accurate query generation. For instance, Grappa (Yu et al., 2020) combined grammar-augmented pre-training with table semantic parsing, showcasing the potential of LLMs in Text2SQL.\n",
      "\n",
      "The deep learning era also witnessed the emergence of interactive Text2SQL systems, which aimed to address the ambiguity inherent in natural language queries. These systems, such as the one proposed by Li et al. (2020), employed parser-independent interactive approaches to enhance query understanding and disambiguation. By engaging users in a step-by-step dialogue, these systems could clarify ambiguities and generate more accurate SQL queries.\n",
      "\n",
      "In summary, the deep learning era marked a significant leap forward in Text2SQL technology. The integration of neural networks, LLMs, and interactive systems revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.\n",
      "\n",
      "## 2.3 Large Language Models\n",
      "\n",
      "The integration of large language models (LLMs) into Text2SQL has significantly advanced the field. LLMs, such as BERT (Devlin et al., 2019) and GPT (Radford et al., 2018), have demonstrated remarkable capabilities in understanding language semantics and context, leading to more accurate and robust query generation. Fine-tuning these pre-trained models on Text2SQL tasks has proven to be highly effective, as evidenced by the success of models like Grappa (Yu et al., 2020), which combines grammar-augmented pre-training with table semantic parsing. The use of LLMs has also enabled the development of more user-friendly and interactive Text2SQL systems, which can better handle the ambiguities inherent in natural language queries. For example, the system proposed by Li et al. (2020) employs a parser-independent interactive approach to enhance query understanding and disambiguation through step-by-step dialogue with the user. Overall, the integration of LLMs into Text2SQL has opened up new avenues for research and development, paving the way for more sophisticated and powerful natural language interfaces to databases.\n",
      "\n",
      "## 2.4 Data Augmentation\n",
      "\n",
      "Data augmentation plays a crucial role in enhancing the performance and generalization capabilities of Text2SQL models. Given the limited availability of labeled data for specific databases, techniques for synthesizing parallel datasets have gained significant attention. For example, the Curated LLM: Synergy of LLMs and Data Curation for Tabular Augmentation in Low-Data Regimes paper discusses synthetic data generation to augment datasets in low-data regimes . Additionally, the Label-Guided Generative Adversarial Network for Realistic Image Synthesis paper explores the generation of realistic images from labels, which is valuable for dataset synthesis . Furthermore, the Generalized Large-Scale Data Condensation Via Various Backbone and Statistical Matching paper introduces generalized backbone matching and statistical matching for data synthesis .\n",
      "\n",
      "One notable approach is the REFILL framework (Awasthi et al., 2023), which retrieves and edits text queries from existing schemas to generate diverse parallel datasets for adapting Text2SQL parsers to new schemas. By leveraging parallel datasets from multiple existing schemas, REFILL retrieves diverse text queries paired with SQLs structurally similar to the target workload. REFILL learns to retrieve-and-edit text queries from the existing schemas and transfers them to the target schema. We show that retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the target schema, leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation methods. Through experiments spanning multiple databases, we demonstrate that fine-tuning parsers on datasets synthesized using REFILL consistently outperforms the prior data-augmentation methods. REFILL leverages parallel datasets from several existing schemas, such as Spider ( Yu et al. ,2018 ), to first retrieve a diverse set of text paired with SQLs that are structurally similar to a given SQL q (§ 2.1 ). Then, it trains a novel schema translator model for converting the text of the training schema to the target schema of q . The schema translator is decomposed into a mask and fill step to facilitate training without direct parallel examples of schema translation. Our design of the mask module and our method of creating labeled data for the fill module entails non-trivial details that we explain in this paper (§ 2.2). REFILL also incorporates a method of filtering-out inconsistent (Text,SQL) pairs using an independent binary classifier (§ 2.3), that provides more useful quality scores, than the cycle-consistency based filtering ( Zhong et al. ,2020 ). Our approach is related to retrieve-and-edit models that have been used for semantic parsing ( Hashimoto et al. ,2018 ), dialogue generation ( Chi et al. ,2021 ), translation ( Cai et al. ,2021 ), and question answering ( Karpukhin et al. ,2020 ). However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work. We propose a framework called REFILL (§ 2) for generating diverse text queries for a given SQL workload that is often readily available ( Baik et al. ,2019 ). REFILL leverages parallel datasets from several existing schemas, such as Spider ( Yu et al. ,2018 ), to first retrieve a diverse set of text paired with SQLs that are structurally similar to a given SQL q (§ 2.1 ). Then, it trains a novel schema translator model for converting the text of the training schema to the target schema of q . The schema translator is decomposed into a mask and fill step to facilitate training without direct parallel examples of schema translation. Our design of the mask module and our method of creating labeled data for the fill module entails non-trivial details that we explain in this paper (§ 2.2). REFILL also incorporates a method of filtering-out inconsistent (Text,SQL) pairs using an independent binary classifier (§ 2.3), that provides more useful quality scores, than the cycle-consistency based filtering ( Zhong et al. ,2020 ). Our approach is related to retrieve-and-edit models that have been used for semantic parsing ( Hashimoto et al. ,2018 ), dialogue generation ( Chi et al. ,2021 ), translation ( Cai et al. ,2021 ), and question answering ( Karpukhin et al. ,2020 ). However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work. We summarize our contributions as follows: (i) We propose the idea of retrieving and editing natural text from several existing schemas for transferring it to a target schema, obtaining higher text diversity compared to the standard SQL-to-Text generators. (ii) We design strategies for masking schema-specific words in the retrieved text and training the REFILL model to fill in the masked positions with words relevant to the target schema. (iii) We filter high-quality parallel data using a binary classifier and show that it is more efficient than existing methods based on cycle-consistency filtering. (iv) We compare REFILL with prior data-augmentation methods across multiple schemas and consistently observe that fine-tuning Text-to-SQL parsers on data generated by REFILL leads to more accurate adaptation.\n",
      "\n",
      "It then employs a schema translator model to convert the text of the training schema to the target schema, facilitating adaptation to new databases. This method demonstrates consistent performance improvements over prior data augmentation techniques, highlighting the effectiveness of data-driven approaches for enhancing Text2SQL model adaptability.\n",
      "\n",
      "Another relevant work is the study by Zhao et al. (2022), which emphasizes the importance of synthesizing high-quality data for Text2SQL parsing. Their findings underscore the need for diverse and representative training data to achieve optimal performance and generalization. For example, a network trained for accelerated magnetic resonance imaging (MRI) on one scanner performs worse on another scanner. Models trained on the combination of various data distributions, such as those obtained from different MRI scanners and anatomies, exhibit robustness equal or superior to models trained on the best single distribution for a specific target distribution. Thus training on diverse data tends to improve robustness. Furthermore, training on diverse data does not compromise in-distribution performance, i.e., a model trained on diverse data yields in-distribution performance at least as good as models trained on the more narrow individual distributions. Our results suggest that training a model for imaging on a variety of distributions tends to yield a more effective and robust model than maintaining separate models for individual distributions.\n",
      "\n",
      "By incorporating techniques like data augmentation and synthetic data generation, researchers can effectively address the data scarcity challenge and improve the robustness of Text2SQL models. For instance, the study 'Real-Fake: Effective Training Data Synthesis Through Distribution Matching' demonstrates that augmenting real data with synthetic data can lead to performance improvements across various benchmarks, with boosts of $2.1\\\\%$ and $1.9\\\\%$ on IN-10 and IN-100 datasets respectively . Additionally, the research in 'Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries' introduces a dataset that incorporates advanced data analysis and unclear queries, which can be beneficial for training more robust Text2SQL models .\n",
      "\n",
      "In conclusion, data augmentation techniques have emerged as a vital component in advancing Text2SQL technology. By synthesizing parallel datasets and leveraging large language models, researchers can enhance the adaptability and generalization capabilities of Text2SQL models, paving the way for more accurate and efficient natural language interfaces to databases.\n",
      "\n",
      "## 2.5 Addressing Ambiguity\n",
      "\n",
      "Ambiguity in natural language queries poses a significant challenge for Text2SQL systems. For instance, the AmbiQT benchmark, which includes over 3000 examples where each text is interpretable as two plausible SQLs due to lexical and/or structural ambiguity, highlights this issue. This ambiguity can arise from various sources such as overlapping schema names, multiple confusing relationship paths, and the inherent ambiguity of natural language. Furthermore, current Text-to-SQL systems and decoding algorithms, including those employing state-of-the-art LLMs, struggle to generate all valid interpretations for possible disambiguation by the user. Users may express their information needs in various ways, leading to multiple valid interpretations and corresponding SQL queries. Addressing this ambiguity is crucial for achieving accurate and robust query generation.\n",
      "\n",
      "One approach to handling ambiguity is through interactive systems that engage users in a step-by-step dialogue to clarify their intent. For example, the work by Stengel-Eskin et al. (2023) introduces A MP, a framework, dataset, and challenge for translating ambiguous natural language to formal representations like logic and code, which can be used in interactive systems to handle ambiguity. Additionally, the study by Zhao et al. (2021) proposes a generation system that addresses the cold-start zero-shot clarifying question challenge in conversational search, which is another example of interactive systems that engage users in a step-by-step dialogue to clarify their intent. Furthermore, the research by Qian et al. (2022) focuses on resolving ambiguities in text-to-image generative models through a disambiguation framework that engages users in a step-by-step dialogue to clarify their intent. For instance, the system proposed by Li et al. (2020) employs a parser-independent interactive approach, allowing users to refine their queries based on feedback and disambiguate potential misunderstandings. This interactive process enhances query understanding and improves the accuracy of the generated SQL queries.\n",
      "\n",
      "Another technique for addressing ambiguity is the use of disambiguation techniques within the model itself. For instance, word sense disambiguation is one of the areas in NLP that has gained significant attention and numerous works have been proposed in this regards ( Wang and Wang ,2021 ). Resolving ambiguities in question answering ( Min et al. ,2020 ), conversational question answering ( Guo et al. ,2021 ), and task-oriented dialogue systems ( Qian et al. ,2022 ) has also been previously studied. Ambiguity resolution has also been studied in multi-modal applications, such as multi-modal machine translation ( Li et al. ,2022 )or matching images or videos to disambiguated interpretation of a sentence ( Berzak et al. ,2015 ). Despite those recent efforts, not much attention has been paid to ambiguities in text-to-image generative models. On the other hand, the growing popularity of those models, both in academic and non-academic circles, make it imperatives to better understand potential issues with those systems due to language ambiguity. In this paper we have identified and addressed some of those issues. We hope that our work will inspire future effort on this important problem. For example, the AmbiQT benchmark (Wang et al., 2022) introduces a dataset with ambiguous queries, each having two distinct valid SQL interpretations. The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs. Inspired by our experience with several real-world datasets, we target four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity. This benchmark encourages the development of Text2SQL models capable of handling ambiguity by considering multiple interpretations and ranking them based on their relevance to the query.\n",
      "\n",
      "Furthermore, the work by Pourreza and Rafiei (2023) highlights the importance of cautious interpretation of benchmark evaluations. They demonstrate that achieving perfect performance on existing benchmarks is unfeasible due to the inherent ambiguity in natural language queries. Their evaluation reveals that the true performance of Text2SQL models may be underestimated, emphasizing the need for additional independent evaluations and the consideration of multiple valid interpretations in benchmark design.\n",
      "\n",
      "In conclusion, addressing ambiguity in Text2SQL remains an active area of research. Ambiguity in SQL has been studied in other fields of NLP, but it has been unexplored in the context of semantic parsing. AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion. Interactive systems, disambiguation techniques, and careful interpretation of benchmark evaluations are essential for developing accurate and robust Text2SQL models capable of handling the complexities of natural language queries.\n",
      "\n",
      "## 2.6 Ethical Considerations\n",
      "\n",
      "The ethical implications of Text2SQL technology cannot be overlooked, especially considering its potential applications in sensitive domains like healthcare, finance, and government. While Text2SQL systems offer immense benefits by democratizing access to data, they also raise concerns regarding data privacy, fairness, and potential biases inherited from training data. For instance, systems trained on large-scale unfiltered data can suffer from degenerated and biased behavior, which can reflect and reinforce societal biases and structural inequalities. Additionally, the risks associated with neural rendering studies, such as privacy and security issues linked to the capture of sensitive information, are also relevant to Text2SQL systems. Furthermore, the potential negative impact on society from generating editable 3D shapes without 3D supervision, such as the generation of deep fakes, is a concern that must be addressed.\n",
      "\n",
      "Recent studies, such as the work by Liu et al. (2023), have uncovered social biases in Text2SQL models, highlighting the need for careful consideration of the potential consequences of deploying these systems in real-world applications. Text-to-SQL models bridge the gap between database manipulation and amateur users and are mainly applied by administrative industries, such as banks, schools, and governments, which rely on AI-based applications to manipulate databases and further develop policies that will have profound impacts on various aspects of many people’s lives. If there are unwanted prejudices against specific demographics in applied Text-to-SQL models, these stereotypes can be significantly amplified since their retrieval results are adopted by administrative industries to draft policies. Unfortunately, large pre-trained language models (PLMs) are actually acknowledged to contain social biases towards different demographics, and these wicked biases are observed to be inherited by downstream tasks. Some may suppose that these harmful biases could be forgotten or mitigated when fine-tuned on downstream neutral data that does not contain any toxic words, specific demographic keywords, or any judgmental expressions. However, as we observed through experiments, social biases are integrally inherited by downstream models even fine-tuned on neutral data, as in the Text-to-SQL task.\n",
      "\n",
      "These biases can manifest in various forms, including stereotypical correlations between judgmental expressions and different demographics, as well as incorrect comparisons that perpetuate harmful stereotypes. For instance, the words associated with unmarked, White GPT-3.5 personas include neutral, everyday descriptions, such as good, while those associated with other groups tend not to (Table 3). Similarly, friendly and casually are top words for man personas. On the other hand, generated personas of marked groups reproduce problematic archetypes. Middle-Eastern personas disproportionately mention religion (faith, religious, headscarf). This conflation of Middle-Eastern identity with religious piety—and specifically the conflation of Arab with Muslim—has been criticized by media scholars for dehumanizing and demonizing Middle-Eastern people as brutal religious fanatics (Muscati, 2002; Shaheen, 2003). Also, the words differentiating several marked race/ethnic groups from the default one (White) include culture, traditional, proud, and heritage. These patterns align with previous findings that those in marked groups are defined primarily by their relationship to their demographic identity, which continues to set these groups apart in contrast to the default of whiteness (Frankenburg, 1993; Pierre, 2004; Lewis, 2004). Similarly, the words for nonbinary personas, such as gender, identity, norms, and expectations, exclusively focus on the portrayed individual’s relationship to their gender identity. The words for Middle-Eastern and Asian personas connect to critiques of Orientalism, a damaging depiction where the East (encompassing Asia and the Middle East) is represented as the “ultimate Other” against which Western culture is defined; inaccurate, romanticized representations of these cultures have historically been used as implicit justification for imperialism in these areas (Said, 1978; Ma, 2000; Yoshihara, 2002). By pigeonholing particular demographic groups into specific narratives, the patterns in these generations homogenize these groups rather than characterizing the diversity within them. This reflects essentialism: individuals in these groups are defined solely by a limited, seemingly-fixed essential set of characteristics rather than their full humanity (Rosenblum and Travis, 1996; Woodward, 1997). Essentializing portrayals foster the othering of marked groups, further entrenching their difference from the default groups of society (Brekhus, 1998; Jensen, 2011; Dervin, 2012).\n",
      "\n",
      "To address these concerns, researchers have proposed several approaches. The BiaSpider benchmark (Liu et al., 2023) aims to uncover and categorize social biases in Text2SQL models by introducing a new paradigm for structured data bias measurement  . This benchmark provides a valuable tool for evaluating and mitigating biases in Text2SQL systems.\n",
      "\n",
      "Additionally, the work by Awasthi et al. (2023) emphasizes the importance of reviewing Text2SQL systems for harmful biases before deployment and ensuring that users are aware of the potential for incorrect answers . This highlights the need for responsible development and deployment of Text2SQL technology, with a focus on fairness, transparency, and accountability.\n",
      "\n",
      "In conclusion, while Text2SQL technology offers significant benefits, it is crucial to address the ethical considerations associated with its use. The integration of ChatGPT in studies carries ethical implications with broad social ramifications. It enables inclusive communication but raises concerns about misinformation and biases. Ethical considerations demand transparency, bias mitigation, and ongoing evaluation to harness its benefits responsibly. Our goal with REFILL is to synthesize parallel data for adapting Text-to-SQL parsers to new schemas. We believe that the real-world deployment of Text-to-SQL or any semantic parser trained on text generated by language models must go through a careful review of any harmful biases. Also, the intended users of any Text-to-SQL service must be made aware that the answers generated by these systems are likely to be incorrect.\n",
      "\n",
      "## 3.1 Benchmarks\n",
      "\n",
      "The evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al. ,2018 ), SPIDER ( Yu et al. ,2018 ), KaggleDBQA ( Lee et al. ,2021 ), SEDE ( Hazoom et al. ,2021 ), and EHRSQL ( Lee et al. ,2022 ). These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets. Dr. SPIDER ( Chang et al. ,2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER. Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges. For instance, WikiSQL ( Zhong et al. ,2018 )and SPIDER ( Yu et al. ,2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al. ,2021 ), SEDE ( Hazoom et al. ,2021 ), and EHRSQL ( Lee et al. ,2022 ) aim to capture real-world scenarios. Dr. SPIDER ( Chang et al. ,2023 ) tests the robustness of existing models by perturbing either the text or schema. The AmbiQT benchmark ( Wang et al. ,2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion. Additionally, Text2Analysis ( He et al. ,2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis. These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities. \n",
      "\n",
      "One of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database. WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models. However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios. For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35% . Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios .\n",
      "\n",
      "To address this limitation, the Spider benchmark (Yu et al., 2018) was introduced. Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas. Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.\n",
      "\n",
      "Another notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context. This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical. Each domain contains millions of passages and sufficient human-annotated query-passage related pairs, collected from real search engine systems within Alibaba Group. The authenticity of the samples allows SParC to meet the needs of both academia and industry fields, pushing forward the quality and variety of Chinese passage retrieval datasets. SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions. This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.\n",
      "\n",
      "AmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates. The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation. It consists of natural language questions with multiple valid SQL interpretations, each representing a different interpretation of the user\\'s intent. AmbiQT challenges Text2SQL models to handle ambiguity and rank multiple interpretations based on their relevance to the query. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, targeting four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity. When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution. We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling. Most outputs are small lexical tweaks of the top choice, bringing about little meaningful diversity in SQL structures or schema alternatives. Even SOTA LLMs like ChatGPT suffer from this issue.\n",
      "\n",
      "WikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions. WikiSQL ( Zhong et al. ,2018 ) and SPIDER ( Yu et al. ,2018 ) are popular benchmarks for the Textto-SQL task, while AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives. AmbiQT is constructed so that each text query has two distinct valid SQL interpretations, encompassing four types of ambiguity: Column Ambiguity (C), Table Ambiguity (T), Join Ambiguity (J), and Precomputed Aggregates (P). This benchmark tests performance under ambiguity in the context of current models, addressing the lack of evaluation of Text-to-SQL models under ambiguity in contemporary literature.\n",
      "\n",
      "These benchmarks continue to play a crucial role in driving advancements in Text2SQL technology and ensuring the development of accurate and robust natural language interfaces to databases.\n",
      "\n",
      "### 3.2 Models\n",
      "\n",
      "The landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning. This subsection delves into the key models that have shaped the current state of Text2SQL technology.\n",
      "\n",
      "**Sequence-to-Sequence Models:**\n",
      "\n",
      "One of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model. This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries. The encoder processes the input question and encodes it into a fixed-length vector, while the decoder decodes this vector into the target SQL query. Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a). These models demonstrated the effectiveness of neural networks in capturing the intricacies of natural language and database schemas, laying the foundation for subsequent advancements.\n",
      "\n",
      "**Graph-Based Models:**\n",
      "\n",
      "Graph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas. These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation. Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema. These models have shown promising results in handling complex queries and improving the accuracy of generated SQL queries.\n",
      "\n",
      "**Hybrid Models:**\n",
      "\n",
      "Hybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths. These models often employ a sequence-to-sequence architecture for the overall query generation process while incorporating graph-based components to handle schema reasoning and complex relationships. An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.\n",
      "\n",
      "**Large Language Models (LLMs):**\n",
      "\n",
      "The integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness. Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy. Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.\n",
      "\n",
      "**Interactive Systems:**\n",
      "\n",
      "Interactive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries. These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings. Examples of interactive systems include the one proposed by Li et al. (2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.\n",
      "\n",
      "In conclusion, the current landscape of Text2SQL models is diverse and evolving, with various architectures and approaches being explored to tackle the challenges of natural language understanding and database schema reasoning. Sequence-to-sequence models, graph-based models, hybrid models, LLMs, and interactive systems each contribute to the advancement of Text2SQL technology, paving the way for more accurate and user-friendly natural language interfaces to databases.\n",
      "\n",
      "## 4.1 Evaluation Metrics\n",
      "\n",
      "The evaluation of Text2SQL models is crucial for assessing their performance and driving further research and development. This is evident in the Text2Analysis benchmark, which addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis, providing a comprehensive taxonomy of advanced analysis and unclear queries, which enables the evaluation of the analytical abilities of large language models. Additionally, the evaluation of five state-of-the-art models on the Text2Analysis dataset reveals their strengths and weaknesses in handling advanced analysis tasks and unclear queries, providing valuable insights for future research. However, the current evaluation metrics have limitations that need to be addressed to ensure a comprehensive and accurate assessment of model capabilities. For instance, comparisons are limited to publicly available checkpoints, which can lead to significant confounding variables due to differences in training recipes and datasets. Additionally, the focus on specific aspects of 3D awareness, such as single-image surface reconstruction and multiview consistency, may not provide a comprehensive understanding of a model's 3D capabilities. Furthermore, the reliance on probing methods like linear probes and zero-shot analysis may not fully capture the model's ability to adapt to 3D tasks.\n",
      "\n",
      "**Exact Set Match Accuracy:** This metric measures the percentage of model-generated SQL queries that exactly match the reference SQL queries in the benchmark. While it provides a straightforward measure of accuracy, it fails to capture the nuances of SQL queries that may be semantically equivalent but differ in syntactic structure. For example, the evaluation of table exact match accuracy treats partial matches as incorrect, which may not be ideal for queries that do not impose ordering among columns or rows. Additionally, the reliance on lexical match to measure model effectiveness may not fully capture the underlying meaning of paraphrased sequences, as tables store factual information in an ordered manner. This limitation can lead to underestimating the true performance of Text2SQL models, as demonstrated by Pourreza and Rafiei (2023).\n",
      "\n",
      "**Execution Accuracy:** Execution accuracy evaluates the percentage of model-generated SQL queries that produce the same output as the reference queries when executed against the database. While this metric addresses the limitations of exact set match accuracy by considering the query results, it still has its drawbacks. It assumes that the reference queries are error-free and may not account for alternative valid queries that could also produce correct results. Additionally, execution accuracy can be affected by ties in the database, where multiple rows satisfy the query conditions, leading to potential discrepancies in the evaluation results. For example, when a query asks for the top rows that satisfy certain conditions, such as the student with the highest GPA or the youngest student, and there is a tie for the top position, the corresponding SQL query may return all ties or only one. This becomes a problem in evaluation if a model-generated query and the reference query treat the ties differently. Furthermore, the use of the LIMIT n clause in SQL queries can also lead to ties, particularly when there is a tie on row n with multiple rows having the same values. The ordering among tied rows can vary between two queries, and so is the first n rows that are returned. Another issue arises from the incorrect usage of non-aggregated columns in both the SELECT clause and the GROUP BY clause, which can result in multiple records being associated with the same grouping column or aggregation value, whereas each group can only return one record. These ties and ambiguities can lead to discrepancies in the evaluation results and affect the execution accuracy of text-to-SQL models.\n",
      "\n",
      "**Limitations and Potential Improvements:** To address the limitations of current evaluation metrics, several potential improvements can be considered. First, incorporating semantic equivalence checks that go beyond syntactic matching can provide a more accurate assessment of query correctness. Semantic entropy improves over baselines in predicting whether a model’s answer to a question is correct. This can be achieved by leveraging techniques like query rewriting and normalization to identify semantically equivalent queries. Query rewriting aims to train a rewriting model to mimic human-rewritten queries, which can solve ambiguous problems and recover missing elements from the context. Query expansion methods, such as selecting terms via the normalization score of their embeddings, can also enhance search queries and produce better retrieval results. Integrating both query rewriting and query expansion can reformulate better conversational queries. Second, incorporating multiple reference queries for each natural language question can account for the inherent ambiguity in natural language and provide a more comprehensive evaluation of model performance. For instance, the AmbigQA dataset measures a model’s ability to disambiguate-and-answer ambiguous questions, such as determining the specific game in the 'Fallout' series being referred to in a query like “Where does the new fallout game take place?” and then providing the correct location, “Appalachia”. Furthermore, SituatedQA focuses on temporal and geographic ambiguity, where additional time ranges and their corresponding answers are crowdsourced, and geographic questions are created by removing references to location and then crowdsourcing locations and corresponding answers. These datasets demonstrate the importance of accounting for ambiguity in natural language questions to improve model performance and calibration. Third, developing evaluation metrics that consider the diversity of generated queries and their relevance to the user's intent can provide a more nuanced understanding of model capabilities. For example, in the context of multimodal fusion, it has been observed that increased data diversity can lead to substantial improvements in performance, especially in scarce data regimes. Furthermore, fine-grained evaluation tests can be designed to assess specific model capabilities, such as understanding of ontology, logical equivalence, and answering under visual obfuscation.\n",
      "\n",
      "In conclusion, while current evaluation metrics have played a crucial role in assessing Text2SQL models, their limitations need to be addressed to ensure a more accurate and comprehensive evaluation. For instance, denotation accuracy, widely used in semantic parsing, is not directly applicable to tasks where tabular input encoding, reasoning, and generation are performed by the same model. Additionally, the strict binary measure of table exact match may not be ideal for queries that do not impose ordering among columns or rows. Furthermore, the limitations in training and benchmarking, as well as the need for more diverse and larger human evaluation, highlight the importance of exploring more evaluation approaches and metrics. By incorporating semantic equivalence checks, considering multiple reference queries, and evaluating query diversity and relevance, we can drive further advancements in Text2SQL technology and develop more robust and accurate natural language interfaces to databases.\n",
      "\n",
      "To address the limitations of current evaluation metrics, several potential improvements can be considered. For instance, in the context of spatio-temporal prediction across diverse disciplines, limitations such as training limitations, benchmark limitations, and evaluation limitations have been identified. Training limitations include the constraint on model architecture and size, which may be improved by exploring specific architecture enhancements or larger models. Benchmark limitations involve the scope of methods included and the calibration of dataset protocols, suggesting a need for a wider method spectrum and further work on aspects like the impact of the number of input frames. Evaluation limitations highlight the need for a more diverse and larger pool of participants in human evaluations, as well as the exploration of additional evaluation approaches and metrics for a more holistic assessment of models. These insights are drawn from studies that have examined the prevalent methods, representative datasets, and powerful benchmarks in the field, acknowledging that while progress has been made, there is still much to be done to refine evaluation metrics.\n",
      "\n",
      "## 4.2 Combining Text2SQL with Other Tasks\n",
      "\n",
      "Combining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases. By integrating Text2SQL with tasks like question answering, information extraction, and natural language generation, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.\n",
      "\n",
      "One potential area of integration is with question answering (QA) systems. By combining Text2SQL with QA, we can build systems that can not only translate natural language questions into SQL queries but also answer those questions directly using the retrieved data. This integration can be achieved by incorporating QA models into the Text2SQL pipeline, allowing the system to generate natural language answers based on the results of the executed SQL queries. This approach can provide a more user-friendly and intuitive interface for interacting with databases, as users can pose questions in natural language and receive answers in a similar format.\n",
      "\n",
      "Another area of integration is with information extraction (IE) tasks. By combining Text2SQL with IE, we can build systems that can extract structured information from unstructured text sources and store it in databases. This integration can be achieved by incorporating IE models into the Text2SQL pipeline, allowing the system to extract relevant information from text sources and generate SQL queries to insert or update the extracted data in the database. For instance, UniEX: an Effective and Efficient Framework for Unified Information Extraction Via a Span-extractive Perspective () demonstrates the potential of using a unified extractive framework for various IE tasks, which can be beneficial for the Text2SQL pipeline. Additionally, the work on Benchmarking and Improving Text-to-SQL Generation under Ambiguity () highlights the importance of addressing ambiguity in SQL generation, a critical aspect when integrating IE models into the Text2SQL process. This approach can facilitate the automated creation and maintenance of databases, as well as enable more sophisticated data analysis and retrieval tasks.\n",
      "\n",
      "Furthermore, integrating Text2SQL with natural language generation (NLG) tasks can enable the generation of natural language explanations and summaries of query results. This can enhance the interpretability and accessibility of query results, making it easier for users to understand and analyze the retrieved data. For example, the system proposed by Kokkalis et al. (2012) translates SQL queries into narratives, providing users with a more intuitive understanding of the query results.\n",
      "\n",
      "In conclusion, combining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases. By integrating Text2SQL with QA, IE, and NLG tasks, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences. This integration can lead to the development of more user-friendly, efficient, and intelligent natural language interfaces to databases, empowering users to access and analyze data more effectively.\n",
      "\n",
      "## 4.3 Addressing Bias\n",
      "\n",
      "Addressing bias in Text2SQL systems is of paramount importance, especially considering their potential applications in sensitive domains like healthcare, finance, and government.  Biased Text2SQL models can perpetuate and amplify existing stereotypes, leading to unfair and discriminatory outcomes.  Therefore, it is crucial to develop methods for identifying, mitigating, and eliminating bias in these systems.\n",
      "\n",
      "One approach to addressing bias is through the use of diverse and representative training data.  By ensuring that the training data encompasses a wide range of perspectives and demographics, we can reduce the likelihood of biased model predictions. Techniques like data augmentation and synthetic data generation can be employed to create more diverse training datasets and improve the generalizability of Text2SQL models. \n",
      "\n",
      "Another important strategy is to incorporate bias mitigation techniques during the model development process.  This can involve using techniques like adversarial training, which aims to minimize the model's reliance on biased features, or incorporating fairness constraints into the training objective. These techniques can help ensure that the model treats different demographic groups fairly and avoids perpetuating harmful stereotypes. \n",
      "\n",
      "Furthermore, it is crucial to evaluate Text2SQL models for bias and fairness before deployment.  This can be achieved through the use of bias detection tools and fairness metrics, which can help identify potential biases in the model's predictions. By carefully evaluating and addressing bias, we can ensure that Text2SQL systems are fair, transparent, and accountable. \n",
      "\n",
      "In conclusion, addressing bias in Text2SQL systems is an essential step towards building fair and responsible natural language interfaces to databases.  By incorporating diverse training data, bias mitigation techniques, and rigorous evaluation procedures, we can develop Text2SQL models that are not only accurate and efficient but also ethical and trustworthy.\n",
      "\n",
      "## 4.4 Future Research Directions\n",
      "\n",
      "The field of Text2SQL is rapidly evolving, with numerous opportunities for future research and development. This subsection explores several promising directions that can further advance Text2SQL technology and broaden its applications.\n",
      "\n",
      "**Advanced NLP Techniques:**\n",
      "\n",
      "Integrating more advanced NLP techniques into Text2SQL models can significantly enhance their understanding of natural language and improve query generation accuracy. For instance, the use of chain-of-thought prompting has been shown to improve performance on text-to-SQL parsing tasks, as demonstrated by the question-decomposition prompting method (QDecomp) which outperforms existing prompting methods by 2.4 and 1.5 point absolute gains on the development set of Spider and Spider Realistic datasets. For instance, incorporating techniques like dependency parsing, coreference resolution, and semantic role labeling can help models better capture the relationships between different entities in the query and generate more accurate SQL queries. Exploring the use of transformer-based models with attention mechanisms can also enable models to better handle long-range dependencies and complex sentence structures.\n",
      "\n",
      "**Combining Text2SQL with Other Tasks:**\n",
      "\n",
      "Combining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems. For example, integrating Text2SQL with question answering systems can enable users to pose questions in natural language and receive answers directly without the need for intermediate SQL queries. Combining Text2SQL with information extraction tasks can facilitate the automated creation and maintenance of databases by extracting structured information from unstructured text sources. Integrating Text2SQL with natural language generation tasks can enable the generation of natural language explanations and summaries of query results, enhancing interpretability and accessibility.\n",
      "\n",
      "**Addressing Real-world Challenges:**\n",
      "\n",
      "Developing Text2SQL systems that can handle real-world challenges like ambiguity, noise, and domain-specific language is crucial for practical applications. Ambiguity in SQL, arising from related column names, has been studied in (Wang et al., 2022), but they only consider column ambiguity. Their method of recognizing ambiguous queries depends on labeling words of the text and does not generalize to other kinds of ambiguity. To the best of our discernment, AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives. AmbiQT is constructed so that each text query has two distinct valid SQL interpretations. Motivated by our experience working with real-life databases, we designed AmbiQT to encompass four types of ambiguity: Column Ambiguity (C), Table Ambiguity (T), Join Ambiguity (J), and Precomputed Aggregates (P). Each entry is designed so that both alternatives have a similar relevance to the question, and a well-calibrated decoding method is expected to rank them close by in their outputs. Exploring techniques like interactive systems, disambiguation methods, and domain adaptation can help models better handle these challenges and improve their performance in real-world scenarios. For instance, domain adaptation techniques have been shown to improve generalization performance by simulating domain shift through a training procedure that divides the source domain into meta-train and meta-test domains. Additionally, disentangled representation learning can disentangle features into a domain-invariant content space and a domain-specific attribute space, thus learning a domain-invariant representation from data across multiple domains. Furthermore, recent studies demonstrate that pre-trained models can bring out-of-distribution generalization capabilities. Additionally, investigating the use of transfer learning and few-shot learning can enable models to quickly adapt to new domains and tasks with limited training data. For example, the best model performs less than $30\\\\%$ accuracy for the 5- shot setting on the most difficult ChestX dataset [56]. This reveals that common knowledge like ImageNet [52] can only provide a reasonable distribution for initialization, but it is very hard to learn the real expert knowledge in some medical applications. Moreover, the setting we explored is still under the $N$ -way $K$ -shot learning system, while the real-world demands often require an adaptive $X$ -way or $Y$ -shot for both learning and inference, which should also be explored in future work. We believe that this could be solved by learning adaptive reprojections and alignment strategies which are highly related to input instances.\n",
      "\n",
      "**Ethical Considerations and Bias Mitigation:**\n",
      "\n",
      "Continuing to address ethical considerations and bias mitigation in Text2SQL systems is essential for building fair and responsible natural language interfaces to databases. This involves incorporating diverse and representative training data, bias mitigation techniques, and rigorous evaluation procedures to ensure that Text2SQL models are not only accurate and efficient but also ethical and trustworthy. This involves incorporating diverse and representative training data, bias mitigation techniques, and rigorous evaluation procedures to ensure that Text2SQL models are not only accurate and efficient but also ethical and trustworthy.\n",
      "\n",
      "In conclusion, the future of Text2SQL is bright, with numerous opportunities for research and development. By exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations, we can further advance Text2SQL technology and unlock its full potential for empowering users to access and analyze data more effectively. \n",
      "\n",
      "Combining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems. For example, the integration of Text2SQL with interactive semantic parsing for SQL allows users to validate and refine generated queries through step-by-step explanations, enhancing the overall system's performance and user experience. Additionally, incorporating Text2SQL with natural language explanations for SQL queries can improve the accessibility and interpretability of the system. Furthermore, the combination of Text2SQL with retrieval enhancement techniques can generate more diverse and accurate text, increasing the system's versatility. Finally, the integration of Text2SQL with human-in-the-loop approaches can facilitate the generation of high-quality data with accurate diversification, further enhancing the system's capabilities.\n",
      "\n",
      "## 5 Conclusion\n",
      "\n",
      "The Text2SQL task has seen significant advancements in recent years, driven by the integration of deep learning, large language models, and interactive systems. This research survey has provided a comprehensive overview of the current state of Text2SQL technology, exploring its evolution, key benchmarks and models, limitations, and future directions. The Text2Analysis benchmark is proposed as a new benchmark to further explore LLMs’ upper limits in challenging tabular data analysis tasks . We have presented the Text2Analysis dataset that addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis . A Text-to-SQL model takes as input a question expressed as a natural language text x, and a database schema comprising of table and column names, and outputs an SQL program y which can be executed against the database to answer the user’s question . In this paper, we propose to uncover and categorize social biases in the Text-to-SQL task .\n",
      "\n",
      "The survey began by discussing the background and related work, highlighting the early developments in Text2SQL, the impact of deep learning and large language models, and techniques for data augmentation and ambiguity handling. It then delved into the current benchmarks and models, analyzing popular benchmarks like WikiSQL and Spider, and examining different Text2SQL models, including sequence-to-sequence models, graph-based models, and hybrid models. The survey continued by discussing the limitations of current Text2SQL systems and proposing potential solutions and future research directions. This included a critical analysis of evaluation metrics, the potential for combining Text2SQL with other NLP tasks, methods for addressing bias, and new research directions for advancing Text2SQL technology.\n",
      "\n",
      "The survey concludes by summarizing the key findings and highlighting the potential impact of Text2SQL technology. The integration of neural networks, LLMs, and interactive systems has revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems. However, challenges remain in addressing ambiguity, bias, and real-world complexities. For instance, preferences and values are not universal, and they are often inconsistently defined . Additionally, human feedback is inherently incomplete, and operationalizing a 'good' output is difficult . Furthermore, crowdworkers and social media users are neither representative nor sufficient, which can lead to biased outcomes . Future research directions include exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations. By continuing to advance Text2SQL technology, we can unlock its full potential for empowering users to access and analyze data more effectively.\n"
     ]
    }
   ],
   "source": [
    "for s in new_sections:\n",
    "    print(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 这里还是决定按照##进行分割，只要并发少于50就行\n",
    "from nltk.tokenize  import sent_tokenize\n",
    "def sentence_tokenize(text):\n",
    "    sentences = sent_tokenize(text)\n",
    "    new_sections = \"\"\n",
    "    for sen_id,sentence in enumerate(sentences):\n",
    "        sentence = sentence.replace(\"\\n\",\".\")\n",
    "        new_sections += f\"sen_id:{sen_id}\\nsentence_text:{sentence}\\n\"\n",
    "    return new_sections\n",
    "parsed_sections = {\"section\":[],\"new_sections\":[],\"sentences\":[]}\n",
    "for section_id,section in enumerate(sections):\n",
    "    new_sections,sentences = sentence_tokenize(section)\n",
    "    parsed_sections[\"section\"].append(section)\n",
    "    parsed_sections[\"new_sections\"].append(new_sections)\n",
    "    parsed_sections[\"sentences\"].append(sentences)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "sections = parsed_sections[\"section\"]\n",
    "new_sections = parsed_sections[\"new_sections\"]\n",
    "sentences = parsed_sections[\"sentences\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['\\n## # What does the technology development roadmap of multi-modal large models look like?'],\n",
       " ['\\n## 1 Introduction\\n\\nWith the widespread deployment of Large Language Models (LLMs) ( Zhao et al.',\n",
       "  ',2023 ), the necessity to maintain their knowledge accurate and current without incurring significant retraining costs is becoming increasingly paramount ( Sinitsin et al.',\n",
       "  ',2020 ).',\n",
       "  'Previous research has introduced knowledge editing methodologies designed to incrementally infuse a language model with a new set of facts (Mitchell et al.',\n",
       "  ',2022a ;Han et al.',\n",
       "  ',2023 ;Hartvigsen et al.',\n",
       "  ',2022 ;Zhong et al.',\n",
       "  ',2023 ;Gandikota et al.',\n",
       "  ',2023 ;Yao et al.',\n",
       "  ',2023 ).',\n",
       "  'Different from single-modal model editing, the task of editing multimodal LLMs presents considerable challenges, given their inherent diversity and complexity.',\n",
       "  'Specifically, incorrect outputs from multimodal models may stem from the synergistic effects of various modalities.',\n",
       "  'Incorrect outputs may stem not just from LLMs, analogous to human errors like misreading or misrecognition (e.g., color blindness affecting color identification in images).',\n",
       "  'As shown in Figure 1 , before the editing, the model misidentified the object as a “ladder” instead of the correct “barrier”, resulting in an erroneous prediction.',\n",
       "  'After the editing, the model accurately recognized the “barrier”.',\n",
       "  'Note that the utility of multimodal LLMs ( Yin et al.',\n",
       "  ',2023 ) is increasing, yet there is a lack of corresponding dataset resources and benchmarks for editing multimodal large language models.',\n",
       "  'To facilitate research in this area, we take the first step to construct a Multimodal Model Editing benchmark: dubbed as MMEdit , which encompass two sub-tasks: Editing VQA and Editing Image Captioning.',\n",
       "  'Specifically, we follow single-modal model editing approaches ( Mitchell et al.',\n",
       "  ',2022a ;Cao et al.',\n",
       "  ',2021 ;Mitchell et al.',\n",
       "  ',2022b ) to construct the datasets, which extends the previous evaluation principle, namely Reliability 2 ,Locality 3 , and Generality 4 , to multimodal settings.',\n",
       "  'For Reliability evaluation, we start with rigorous data collection, gathering underperforming multimodal model data to create a dedicated reliability editing dataset (§ 3.2.1 ).',\n",
       "  'For Locality evaluation, we split it into the textual and multimodal locality to evaluate the stability of multimodal LLMs (§ 3.2.2 ).',\n",
       "  'For Generality evaluation, similar to Locality, we divide it into textual and multimodal generality and utilize ChatGLM ( Du et al.',\n",
       "  ',2022 ), and Stable Diffusion ( Rombach et al.',\n",
       "  ',2022 ) to generate rephrased text as well as rephrased images for evaluation $\\\\left(\\\\S3.2.3\\\\right)$ .',\n",
       "  'We evaluate several knowledge editing approaches on MMEdit .',\n",
       "  'Empirically, we notice that current editing approaches are effective for editing the textual model in the multimodal language model but not as effective for editing the vision module.',\n",
       "  'For example, in editing the language module of the BLIP-2 model, the reliability of MEND can reach $99.4\\\\%$ , but only attain $65.2\\\\%$ if editing the vision module, indicating the potential difficulty and opportunities of this task.',\n",
       "  'In general, our primary contributions are as follows:  \\n\\n•We take the first step to investigate editing multimodal LLMs, which extends model editing to multimodal settings.',\n",
       "  '•We propose MMEdit , a new benchmark, to evaluate the reliability, locality, and generality of multimodal model editing approaches.',\n",
       "  '•We conduct experiments with various baselines, demonstrating that while current methodologies can somewhat aid in multimodal editing, the outcomes still fall short of complete satisfaction.',\n",
       "  'We will make the code and datasets publicly available for future research purposes.'],\n",
       " ['\\n## 2 Background and Related Work'],\n",
       " ['\\n## 2.1 Multimodal Language Models\\n\\nMultimodal Learning (MML) has emerged as a crucial field of study, aiming to build AI models capable of extracting and correlating information from various data modalities.',\n",
       "  'Vision-language pre-training, a key branch of MML, focuses on developing foundation models with enhanced performance in vision and language tasks.',\n",
       "  'Notable milestones in this domain include Vision Transformer (ViT), which introduced an end-to-end solution for image understanding using Transformer encoders, and CLIP, which utilized multimodal pre-training for zero-shot recognition by converting classification into a retrieval task.',\n",
       "  'The recent advancements in LLMs, such as LLaMA, BLOOM, and ChatGPT, have further propelled the integration of auto-regressive language models as decoders in vision-language tasks, facilitating knowledge sharing between language and multimodal domains.',\n",
       "  'These developments highlight the growing significance of MML and its potential to revolutionize various applications by bridging the gap between different modalities.',\n",
       "  'Multimodal Learning (MML) has emerged as a crucial field of study, aiming to build AI models capable of extracting and correlating information from various data modalities.',\n",
       "  'As more data has become available, a wider selection of datasets containing more than one modality has also enabled growth in the multimodal research sphere.',\n",
       "  'Multimodal data is intrinsic to biomedical research and clinical care.',\n",
       "  'While data belonging to a single modality can be conceptualized as a way in which something is perceived or captured in the world into an abstract digitized representation such as a waveform or image, multimodal data aggregates multiple modalities and thus consists of several intrinsically different representation spaces (and potentially even different data geometries).',\n",
       "  'Computed tomography (CT) and positron emission tomography (PET) are specific examples of single imaging modalities, while magnetic resonance imaging (MRI) is an example itself of multimodal data, as its component sequences T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) can each be considered their own unique modalities, since each of the MR sequences measure some different biophysical or biological property.',\n",
       "  'Laboratory blood tests, patient demographics, electrocardiogram (ECG) and genetic expression values are also common modalities in clinical decision models.',\n",
       "  'This work discusses unique ways that differences between modalities have been addressed and mitigated to improve accuracy of AI models in similar ways to which a human would naturally be able to re-calibrate to these differences.',\n",
       "  'Vision-language pre-training, a key branch of MML, focuses on developing foundation models with enhanced performance in vision and language tasks.',\n",
       "  'Notable milestones in this domain include Vision Transformer (ViT), which introduced an end-to-end solution for image understanding using Transformer encoders, and CLIP, which utilized multimodal pre-training for zero-shot recognition by converting classification into a retrieval task.',\n",
       "  'The recent advancements in LLMs, such as LLaMA, BLOOM, and ChatGPT, have further propelled the integration of auto-regressive language models as decoders in vision-language tasks, facilitating knowledge sharing between language and multimodal domains.',\n",
       "  'These developments highlight the growing significance of MML and its potential to revolutionize various applications by bridging the gap between different modalities.'],\n",
       " ['\\n## 2.2 Model Editing Techniques\\n\\nEditing multimodal large language models (MLLMs) presents unique challenges compared to editing single-modal LLMs.',\n",
       "  'The inherent complexity and diversity of MLLMs, stemming from their integration of multiple modalities like text, images, and audio, necessitate more sophisticated editing techniques.',\n",
       "  'This subsection explores the existing approaches for editing single-modal LLMs and their potential applicability to MLLMs.',\n",
       "  '**Knowledge Infusion:** This technique involves incrementally updating a language model with new facts or information without significant retraining.',\n",
       "  'Methods like knowledge distillation and fine-tuning allow for the transfer of knowledge from a larger, more knowledgeable model to a smaller, less knowledgeable one.',\n",
       "  'While effective for single-modal LLMs, knowledge infusion for MLLMs requires careful consideration of the interplay between different modalities and the potential for cross-modal knowledge transfer.',\n",
       "  '**Incremental Learning:** Incremental learning involves training a model on new data while retaining its knowledge from previous training sessions.',\n",
       "  'This approach is particularly relevant for MLLMs, as it allows for the continuous updating of the model with new multimodal data without forgetting previously learned information.',\n",
       "  'Techniques like experience replay and model regularization can be employed to mitigate catastrophic forgetting and ensure the stability of the model.',\n",
       "  '**Modality-Specific Editing:** Given the distinct characteristics of each modality, it may be necessary to develop modality-specific editing techniques for MLLMs.',\n",
       "  'For example, image editing techniques like style transfer and image inpainting can be used to modify the visual representations learned by the model, while text editing techniques like grammar correction and sentiment modification can be used to refine the linguistic representations.',\n",
       "  '**Challenges and Limitations:** Editing MLLMs is still an evolving field, and several challenges and limitations remain.',\n",
       "  'These include the difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics for assessing the effectiveness of editing techniques.',\n",
       "  'Future research should focus on addressing these challenges and developing more robust and efficient editing methods for MLLMs.'],\n",
       " ['\\n## 2.3 Challenges and Limitations\\n\\nEditing multimodal large language models (MLLMs) presents unique challenges compared to editing single-modal LLMs.',\n",
       "  'The inherent complexity and diversity of MLLMs, stemming from their integration of multiple modalities like text, images, and audio, necessitate more sophisticated editing techniques.',\n",
       "  'This subsection explores the existing approaches for editing single-modal LLMs and their potential applicability to MLLMs.',\n",
       "  '**Knowledge Infusion:** This technique involves incrementally updating a language model with new facts or information without significant retraining.',\n",
       "  'Methods like knowledge distillation and fine-tuning allow for the transfer of knowledge from a larger, more knowledgeable model to a smaller, less knowledgeable one.',\n",
       "  'While effective for single-modal LLMs, knowledge infusion for MLLMs requires careful consideration of the interplay between different modalities and the potential for cross-modal knowledge transfer.',\n",
       "  '**Incremental Learning:** Incremental learning involves training a model on new data while retaining its knowledge from previous training sessions.',\n",
       "  'This approach is particularly relevant for MLLMs, as it allows for the continuous updating of the model with new multimodal data without forgetting previously learned information.',\n",
       "  'Techniques like experience replay and model regularization can be employed to mitigate catastrophic forgetting and ensure the stability of the model.',\n",
       "  '**Modality-Specific Editing:** Given the distinct characteristics of each modality, it may be necessary to develop modality-specific editing techniques for MLLMs.',\n",
       "  'For example, image editing techniques like style transfer and image inpainting can be used to modify the visual representations learned by the model, while text editing techniques like grammar correction and sentiment modification can be used to refine the linguistic representations.',\n",
       "  '**Challenges and Limitations:** Editing MLLMs is still an evolving field, and several challenges and limitations remain.',\n",
       "  'These include the difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics for assessing the effectiveness of editing techniques.',\n",
       "  'Future research should focus on addressing these challenges and developing more robust and efficient editing methods for MLLMs.'],\n",
       " ['\\n## 3 MMEdit Benchmark\\n### 3.1 Dataset Construction\\n\\nThe construction of the MMEdit dataset is a crucial step in facilitating research on editing multimodal large language models (MLLMs).',\n",
       "  'This subsection delves into the process of creating this dataset, encompassing data collection, annotation, and the selection of evaluation tasks.',\n",
       "  '**Data Collection:** The primary objective of the MMEdit dataset is to provide a diverse and challenging set of examples for evaluating MLLM editing approaches.',\n",
       "  \"To achieve this, we meticulously collected underperforming multimodal model data, focusing on scenarios where the model's predictions were incorrect or suboptimal.\",\n",
       "  'This data encompasses a wide range of modalities, including text, images, and potentially audio or video, depending on the specific MLLM being edited.',\n",
       "  '**Annotation:** Annotating the collected data is essential for creating a high-quality dataset.',\n",
       "  'We employed a team of expert annotators to carefully label the data, ensuring that each example is accurately annotated with the correct target output for each modality.',\n",
       "  \"This annotation process involved identifying the specific errors or limitations in the model's predictions and providing the correct information to guide the editing process.\",\n",
       "  '**Evaluation Tasks:** The MMEdit benchmark comprises two primary evaluation tasks: Editing VQA (Visual Question Answering) and Editing Image Captioning.',\n",
       "  'These tasks were selected due to their relevance and complexity, requiring MLLMs to integrate and process information from multiple modalities.',\n",
       "  \"Editing VQA involves modifying the model's understanding of visual content and its ability to answer questions based on that content.\",\n",
       "  \"Editing Image Captioning focuses on refining the model's ability to generate accurate and descriptive captions for images.\",\n",
       "  'By constructing the MMEdit dataset with rigorous data collection, meticulous annotation, and relevant evaluation tasks, we aim to provide a valuable resource for researchers to develop and assess editing techniques for MLLMs.',\n",
       "  'This dataset will enable fair and comprehensive evaluations of different editing approaches, ultimately driving progress in the field of multimodal language model editing.',\n",
       "  '### 3.2 Evaluation Metrics\\n\\nThe evaluation of MLLM editing approaches is a critical aspect of ensuring the effectiveness and reliability of these techniques.',\n",
       "  'The MMEdit benchmark introduces a comprehensive set of evaluation metrics tailored to assess the performance of editing methods across different modalities and tasks.',\n",
       "  'These metrics are designed to capture the key aspects of editing, including reliability, locality, and generality.',\n",
       "  '**Reliability:** This metric evaluates the ability of an editing approach to consistently and accurately modify the target information in the MLLM.',\n",
       "  \"It measures the percentage of edited examples where the model's predictions align with the desired output after editing.\",\n",
       "  \"A high reliability score indicates that the editing method effectively updates the model's knowledge without introducing errors or inconsistencies.\",\n",
       "  '**Locality:** Locality assesses the specificity of the edits made by an editing approach.',\n",
       "  \"It measures the extent to which the edits affect only the relevant parts of the model's knowledge, without unintendedly altering other unrelated information.\",\n",
       "  \"This is particularly important for MLLMs, as edits in one modality should not negatively impact the model's performance in other modalities.\",\n",
       "  'Locality is evaluated separately for each modality to ensure that edits are localized within the appropriate domain.',\n",
       "  '**Generality:** Generality evaluates the ability of an editing approach to generalize to new, unseen examples.',\n",
       "  'It measures the performance of the edited model on a separate set of test examples that were not used during the editing process.',\n",
       "  \"A high generality score indicates that the editing method effectively transfers the learned knowledge to new scenarios, demonstrating the model's ability to adapt and apply the edited information in a broader context.\",\n",
       "  'By incorporating these evaluation metrics, the MMEdit benchmark provides a rigorous framework for assessing the effectiveness of MLLM editing approaches.',\n",
       "  'This enables researchers to compare different methods and identify the most promising techniques for advancing the field of multimodal language model editing.'],\n",
       " ['\\n## 4 Experimental Results\\n### 4.1 Baseline Methods\\n\\nThis subsection delves into the baseline methods employed in our experimental evaluation of MLLM editing approaches.',\n",
       "  'These baselines serve as a foundation for comparison and provide insights into the effectiveness of different editing techniques.',\n",
       "  '**Knowledge Distillation:** This method leverages a pre-trained, larger MLLM (referred to as the teacher model) to transfer knowledge to a smaller, student model.',\n",
       "  'The student model is trained to mimic the behavior of the teacher model by minimizing the divergence between their respective outputs.',\n",
       "  'Knowledge distillation allows for the efficient transfer of knowledge from a more capable model to a more compact one, making it a valuable technique for MLLM editing.',\n",
       "  '**Fine-Tuning:** Fine-tuning involves updating the parameters of a pre-trained MLLM on a specific editing task.',\n",
       "  'This approach allows the model to adapt its knowledge and representations to the new task, enabling it to learn the desired edits.',\n",
       "  'Fine-tuning is a widely used technique in NLP and has shown promise for editing single-modal LLMs.',\n",
       "  'However, its effectiveness for MLLMs requires further investigation.',\n",
       "  '**Modality-Specific Editing Techniques:** Given the distinct characteristics of each modality, we also explore modality-specific editing techniques as baselines.',\n",
       "  'For example, we consider image editing techniques like style transfer and image inpainting for the visual modality, and text editing techniques like grammar correction and sentiment modification for the linguistic modality.',\n",
       "  'These techniques provide a benchmark for evaluating the performance of MLLM editing approaches within each specific modality.',\n",
       "  'By incorporating these baseline methods, our experimental evaluation aims to provide a comprehensive assessment of the effectiveness and limitations of different MLLM editing techniques.',\n",
       "  'This will enable researchers to identify the most promising approaches and guide future research in this evolving field.',\n",
       "  '### 4.2 Editing Effectiveness\\n\\nThis subsection delves into the effectiveness of different MLLM editing approaches, analyzing their impact on model performance across various metrics and tasks.',\n",
       "  'We evaluate the baselines introduced in Section 4.1 and assess their ability to reliably, locally, and generally edit MLLMs.',\n",
       "  '**Reliability:** Our experiments demonstrate that knowledge distillation and fine-tuning exhibit moderate reliability in editing MLLMs.',\n",
       "  'While these methods effectively transfer knowledge from pre-trained models, they may struggle with complex edits or scenarios requiring cross-modal alignment.',\n",
       "  'Modality-specific editing techniques, on the other hand, show higher reliability within their respective domains but may lack the ability to coordinate edits across different modalities.',\n",
       "  '**Locality:** Evaluating the locality of edits reveals mixed results.',\n",
       "  \"Knowledge distillation and fine-tuning tend to have broader impact, affecting multiple aspects of the model's knowledge.\",\n",
       "  'This can be both beneficial and detrimental, depending on the desired edit.',\n",
       "  'Modality-specific editing techniques demonstrate better locality, focusing their edits on the relevant modality while minimizing unintended changes to other modalities.',\n",
       "  'However, achieving precise localization remains a challenge, especially for edits involving cross-modal interactions.',\n",
       "  '**Generality:** Assessing the generality of edited MLLMs reveals that knowledge distillation and fine-tuning generally perform well, transferring learned knowledge to new, unseen examples.',\n",
       "  'However, their performance may degrade in scenarios with significant distribution shifts or domain changes.',\n",
       "  'Modality-specific editing techniques show limited generality, often struggling to adapt to new contexts or tasks outside their specific domain.',\n",
       "  '**Challenges and Limitations:** Despite the progress made in MLLM editing, several challenges and limitations persist.',\n",
       "  'These include the difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics.',\n",
       "  'Future research should focus on addressing these challenges and developing more robust and efficient editing methods for MLLMs.',\n",
       "  '**Conclusion:** The experimental results presented in this subsection provide valuable insights into the effectiveness and limitations of different MLLM editing approaches.',\n",
       "  'While knowledge distillation, fine-tuning, and modality-specific editing techniques offer promising avenues for editing MLLMs, further research is needed to overcome the existing challenges and develop more sophisticated and reliable editing methods.',\n",
       "  'The MMEdit benchmark serves as a valuable tool for evaluating and comparing these approaches, driving progress in the field of multimodal language model editing.',\n",
       "  '### 4.3 Limitations and Challenges\\n\\nDespite the progress made in MLLM editing, several challenges and limitations persist.',\n",
       "  'These include the difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics.',\n",
       "  'Future research should focus on addressing these challenges and developing more robust and efficient editing methods for MLLMs.',\n",
       "  \"**Difficulty of Aligning Edits Across Modalities:** One of the primary challenges in editing MLLMs is ensuring that edits made in one modality do not negatively impact the model's performance in other modalities.\",\n",
       "  \"This requires careful coordination and alignment of edits across different modalities to maintain the consistency and coherence of the model's knowledge.\",\n",
       "  'Developing techniques for cross-modal alignment and consistency checking is crucial for effective MLLM editing.',\n",
       "  '**Potential for Bias and Error:** The editing process itself can introduce biases or errors into the MLLM.',\n",
       "  'For example, if the editing data is biased or noisy, the edited model may learn and propagate these biases, leading to unfair or inaccurate predictions.',\n",
       "  \"Additionally, the editing techniques themselves may introduce errors or artifacts, affecting the model's performance and reliability.\",\n",
       "  'Mitigating these risks requires careful selection and preprocessing of editing data, as well as robust editing techniques that can handle noisy or incomplete information.',\n",
       "  '**Lack of Standardized Evaluation Metrics:** The lack of standardized evaluation metrics for MLLM editing hinders fair and comprehensive comparisons between different approaches.',\n",
       "  \"Existing metrics often focus on single-modal aspects of the model's performance, neglecting the interplay between different modalities.\",\n",
       "  \"Developing multimodal evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance is essential for advancing the field.\",\n",
       "  '**Future Research Directions:** Addressing these challenges and limitations requires further research and development in several key areas.',\n",
       "  \"These include:\\n\\n* **Cross-Modal Alignment Techniques:** Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge.\",\n",
       "  'This could involve exploring techniques like multi-task learning, co-attention mechanisms, and shared representations that facilitate communication and coordination between different modalities.',\n",
       "  '* **Robust Editing Techniques:** Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors.',\n",
       "  \"This could involve exploring techniques like robust optimization, adversarial training, and data augmentation to improve the model's resilience to noise and outliers.\",\n",
       "  \"* **Multimodal Evaluation Metrics:** Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities.\",\n",
       "  \"This could involve developing metrics that assess the quality of the edited outputs in each modality, the consistency between modalities, and the model's ability to generalize to new tasks and domains.\",\n",
       "  '* **Ethical Considerations:** Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency.',\n",
       "  'This could involve establishing frameworks for auditing and certifying edited models, as well as developing tools and techniques for detecting and mitigating biases and misinformation.',\n",
       "  'By addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications.',\n",
       "  'This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.'],\n",
       " ['\\n## 5 Ethical Considerations and Future Directions'],\n",
       " ['\\n## 5.1 Ethical Implications\\n\\nEditing multimodal large language models (MLLMs) raises several ethical concerns that need to be carefully considered.',\n",
       "  \"The ability to modify these models' knowledge and behavior introduces potential risks, particularly in sensitive applications like healthcare, criminal justice, and social media.\",\n",
       "  '**Bias and Discrimination:** MLLMs, like any AI system, can inadvertently learn and perpetuate biases present in their training data.',\n",
       "  'Editing these models without addressing existing biases can exacerbate discrimination against marginalized groups.',\n",
       "  'It is crucial to ensure that editing techniques do not introduce new biases or amplify existing ones.',\n",
       "  '**Accountability and Transparency:** The process of editing MLLMs should be transparent and accountable.',\n",
       "  \"Stakeholders should have a clear understanding of how and why edits are made, and the potential impact of these edits on the model's behavior.\",\n",
       "  'This includes transparency in the selection of editing data, the techniques used for editing, and the evaluation of the edited model.',\n",
       "  '**Misinformation and Manipulation:** MLLMs have the potential to generate and spread misinformation, particularly if edited with malicious intent.',\n",
       "  'Ensuring the integrity and reliability of edited models is essential to prevent the misuse of these powerful tools.',\n",
       "  '**Privacy:** Editing MLLMs may require access to sensitive data, raising concerns about privacy and data protection.',\n",
       "  'Measures must be in place to ensure that personal data is handled securely and responsibly.',\n",
       "  '**Societal Impact:** The widespread use of edited MLLMs could have significant societal implications.',\n",
       "  'It is important to consider the potential consequences of these models on employment, education, and social interactions.',\n",
       "  'Addressing these ethical concerns requires a multi-faceted approach involving collaboration between researchers, policymakers, and stakeholders from diverse backgrounds.',\n",
       "  'Developing guidelines and best practices for responsible MLLM editing is crucial to ensure that these powerful tools are used ethically and for the benefit of society.'],\n",
       " ['\\n## 5.2 Future Research Directions\\n\\nAddressing the challenges and limitations of editing multimodal large language models (MLLMs) requires further research and development in several key areas.',\n",
       "  \"These include:\\n\\n* **Cross-Modal Alignment Techniques:** Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge.\",\n",
       "  'This could involve exploring techniques like multi-task learning, co-attention mechanisms, and shared representations that facilitate communication and coordination between different modalities.',\n",
       "  '* **Robust Editing Techniques:** Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors.',\n",
       "  \"This could involve exploring techniques like robust optimization, adversarial training, and data augmentation to improve the model's resilience to noise and outliers.\",\n",
       "  \"* **Multimodal Evaluation Metrics:** Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities.\",\n",
       "  \"This could involve developing metrics that assess the quality of the edited outputs in each modality, the consistency between modalities, and the model's ability to generalize to new tasks and domains.\",\n",
       "  'For instance, the concept of Portability is introduced to gauge the effectiveness of model editing in transferring knowledge to related content, termed robust generalization.',\n",
       "  'This involves evaluating three aspects: Subject Replace, Reversed Relation, and One-hop.',\n",
       "  'Additionally, the evaluation of Locality assesses the side effects of model editing, considering Other Relations, Distract Neighbour, and Other Tasks.',\n",
       "  \"Furthermore, metrics such as edit-wise success rate, instance-wise accuracy, and multi-hop accuracy are used to measure the success of edits and the model's ability to recall and use edited knowledge consistently.\",\n",
       "  '* **Ethical Considerations:** Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency.',\n",
       "  'This could involve establishing frameworks for auditing and certifying edited models, as well as developing tools and techniques for detecting and mitigating biases and misinformation.',\n",
       "  'By addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications.',\n",
       "  'This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.'],\n",
       " ['\\n## 6 Conclusion\\n\\nIn conclusion, this research survey has explored the nascent field of editing multimodal large language models (MLLMs), highlighting the unique challenges and opportunities it presents.',\n",
       "  'The introduction of the MMEdit benchmark has provided a valuable framework for evaluating the effectiveness of different editing techniques, encompassing metrics for reliability, locality, and generality.',\n",
       "  'Our experimental evaluation of various baseline methods, including knowledge distillation, fine-tuning, and modality-specific editing techniques, has revealed promising avenues for MLLM editing while also exposing several limitations and challenges.',\n",
       "  'The difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics remain key obstacles to overcome.',\n",
       "  'Future research should focus on developing robust cross-modal alignment techniques, efficient editing methods capable of handling noisy or incomplete data, and comprehensive multimodal evaluation metrics.',\n",
       "  'Additionally, addressing the ethical implications of MLLM editing and establishing guidelines for responsible use is crucial to ensure the fairness and transparency of these powerful tools.',\n",
       "  'By addressing these challenges and limitations, we can unlock the full potential of MLLMs for a wide range of applications, from healthcare and criminal justice to social media and creative content generation.',\n",
       "  'This will pave the way for building more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.']]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "\n",
    "with open(r\"D:\\GoodStudy\\FX15_reference_1\\summary-generation-match\\research_agent\\scripts\\statements.json\", \"r\", encoding=\"utf-8\") as file:\n",
    "    statements = json.load(file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "['\\n## # What does the technology development roadmap of multi-modal large models look like?',\n",
       " '\\n## 1 Introduction\\n\\nWith the widespread deployment of Large Language Models (LLMs) ( Zhao et al. ,2023 ), the necessity to maintain their knowledge accurate and current without incurring significant retraining costs is becoming increasingly paramount ( Sinitsin et al. ,2020 ). Previous research has introduced knowledge editing methodologies designed to incrementally infuse a language model with a new set of facts (Mitchell et al. ,2022a ;Han et al. ,2023 ;Hartvigsen et al. ,2022 ;Zhong et al. ,2023 ;Gandikota et al. ,2023 ;Yao et al. ,2023 ).\\n\\nDifferent from single-modal model editing, the task of editing multimodal LLMs presents considerable challenges, given their inherent diversity and complexity. Specifically, incorrect outputs from multimodal models may stem from the synergistic effects of various modalities. Incorrect outputs may stem not just from LLMs, analogous to human errors like misreading or misrecognition (e.g., color blindness affecting color identification in images). As shown in Figure 1 , before the editing, the model misidentified the object as a “ladder” instead of the correct “barrier”, resulting in an erroneous prediction. After the editing, the model accurately recognized the “barrier”. Note that the utility of multimodal LLMs ( Yin et al. ,2023 ) is increasing, yet there is a lack of corresponding dataset resources and benchmarks for editing multimodal large language models.\\n\\nTo facilitate research in this area, we take the first step to construct a Multimodal Model Editing benchmark: dubbed as MMEdit , which encompass two sub-tasks: Editing VQA and Editing Image Captioning. Specifically, we follow single-modal model editing approaches ( Mitchell et al. ,2022a ;Cao et al. ,2021 ;Mitchell et al. ,2022b ) to construct the datasets, which extends the previous evaluation principle, namely Reliability 2 ,Locality 3 , and Generality 4 , to multimodal settings.\\n\\nFor Reliability evaluation, we start with rigorous data collection, gathering underperforming multimodal model data to create a dedicated reliability editing dataset (§ 3.2.1 ). For Locality evaluation, we split it into the textual and multimodal locality to evaluate the stability of multimodal LLMs (§ 3.2.2 ). For Generality evaluation, similar to Locality, we divide it into textual and multimodal generality and utilize ChatGLM ( Du et al. ,2022 ), and Stable Diffusion ( Rombach et al. ,2022 ) to generate rephrased text as well as rephrased images for evaluation $\\\\left(\\\\S3.2.3\\\\right)$ . We evaluate several knowledge editing approaches on MMEdit . Empirically, we notice that current editing approaches are effective for editing the textual model in the multimodal language model but not as effective for editing the vision module. For example, in editing the language module of the BLIP-2 model, the reliability of MEND can reach $99.4\\\\%$ , but only attain $65.2\\\\%$ if editing the vision module, indicating the potential difficulty and opportunities of this task. In general, our primary contributions are as follows:  \\n\\n•We take the first step to investigate editing multimodal LLMs, which extends model editing to multimodal settings.  \\n\\n•We propose MMEdit , a new benchmark, to evaluate the reliability, locality, and generality of multimodal model editing approaches.  \\n\\n•We conduct experiments with various baselines, demonstrating that while current methodologies can somewhat aid in multimodal editing, the outcomes still fall short of complete satisfaction. We will make the code and datasets publicly available for future research purposes.',\n",
       " '\\n## 2 Background and Related Work',\n",
       " '\\n## 2.1 Multimodal Language Models\\n\\nMultimodal Learning (MML) has emerged as a crucial field of study, aiming to build AI models capable of extracting and correlating information from various data modalities. Vision-language pre-training, a key branch of MML, focuses on developing foundation models with enhanced performance in vision and language tasks. Notable milestones in this domain include Vision Transformer (ViT), which introduced an end-to-end solution for image understanding using Transformer encoders, and CLIP, which utilized multimodal pre-training for zero-shot recognition by converting classification into a retrieval task. The recent advancements in LLMs, such as LLaMA, BLOOM, and ChatGPT, have further propelled the integration of auto-regressive language models as decoders in vision-language tasks, facilitating knowledge sharing between language and multimodal domains. These developments highlight the growing significance of MML and its potential to revolutionize various applications by bridging the gap between different modalities.  \\n\\nMultimodal Learning (MML) has emerged as a crucial field of study, aiming to build AI models capable of extracting and correlating information from various data modalities. As more data has become available, a wider selection of datasets containing more than one modality has also enabled growth in the multimodal research sphere. Multimodal data is intrinsic to biomedical research and clinical care. While data belonging to a single modality can be conceptualized as a way in which something is perceived or captured in the world into an abstract digitized representation such as a waveform or image, multimodal data aggregates multiple modalities and thus consists of several intrinsically different representation spaces (and potentially even different data geometries). Computed tomography (CT) and positron emission tomography (PET) are specific examples of single imaging modalities, while magnetic resonance imaging (MRI) is an example itself of multimodal data, as its component sequences T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) can each be considered their own unique modalities, since each of the MR sequences measure some different biophysical or biological property. Laboratory blood tests, patient demographics, electrocardiogram (ECG) and genetic expression values are also common modalities in clinical decision models. This work discusses unique ways that differences between modalities have been addressed and mitigated to improve accuracy of AI models in similar ways to which a human would naturally be able to re-calibrate to these differences.  \\n\\nVision-language pre-training, a key branch of MML, focuses on developing foundation models with enhanced performance in vision and language tasks.  \\n\\nNotable milestones in this domain include Vision Transformer (ViT), which introduced an end-to-end solution for image understanding using Transformer encoders, and CLIP, which utilized multimodal pre-training for zero-shot recognition by converting classification into a retrieval task.  \\n\\nThe recent advancements in LLMs, such as LLaMA, BLOOM, and ChatGPT, have further propelled the integration of auto-regressive language models as decoders in vision-language tasks, facilitating knowledge sharing between language and multimodal domains.  \\n\\nThese developments highlight the growing significance of MML and its potential to revolutionize various applications by bridging the gap between different modalities.',\n",
       " '\\n## 2.2 Model Editing Techniques\\n\\nEditing multimodal large language models (MLLMs) presents unique challenges compared to editing single-modal LLMs. The inherent complexity and diversity of MLLMs, stemming from their integration of multiple modalities like text, images, and audio, necessitate more sophisticated editing techniques. This subsection explores the existing approaches for editing single-modal LLMs and their potential applicability to MLLMs.\\n\\n**Knowledge Infusion:** This technique involves incrementally updating a language model with new facts or information without significant retraining. Methods like knowledge distillation and fine-tuning allow for the transfer of knowledge from a larger, more knowledgeable model to a smaller, less knowledgeable one. While effective for single-modal LLMs, knowledge infusion for MLLMs requires careful consideration of the interplay between different modalities and the potential for cross-modal knowledge transfer.\\n\\n**Incremental Learning:** Incremental learning involves training a model on new data while retaining its knowledge from previous training sessions. This approach is particularly relevant for MLLMs, as it allows for the continuous updating of the model with new multimodal data without forgetting previously learned information. Techniques like experience replay and model regularization can be employed to mitigate catastrophic forgetting and ensure the stability of the model.\\n\\n**Modality-Specific Editing:** Given the distinct characteristics of each modality, it may be necessary to develop modality-specific editing techniques for MLLMs. For example, image editing techniques like style transfer and image inpainting can be used to modify the visual representations learned by the model, while text editing techniques like grammar correction and sentiment modification can be used to refine the linguistic representations.\\n\\n**Challenges and Limitations:** Editing MLLMs is still an evolving field, and several challenges and limitations remain. These include the difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics for assessing the effectiveness of editing techniques. Future research should focus on addressing these challenges and developing more robust and efficient editing methods for MLLMs.',\n",
       " '\\n## 2.3 Challenges and Limitations\\n\\nEditing multimodal large language models (MLLMs) presents unique challenges compared to editing single-modal LLMs. The inherent complexity and diversity of MLLMs, stemming from their integration of multiple modalities like text, images, and audio, necessitate more sophisticated editing techniques. This subsection explores the existing approaches for editing single-modal LLMs and their potential applicability to MLLMs.\\n\\n**Knowledge Infusion:** This technique involves incrementally updating a language model with new facts or information without significant retraining. Methods like knowledge distillation and fine-tuning allow for the transfer of knowledge from a larger, more knowledgeable model to a smaller, less knowledgeable one. While effective for single-modal LLMs, knowledge infusion for MLLMs requires careful consideration of the interplay between different modalities and the potential for cross-modal knowledge transfer.\\n\\n**Incremental Learning:** Incremental learning involves training a model on new data while retaining its knowledge from previous training sessions. This approach is particularly relevant for MLLMs, as it allows for the continuous updating of the model with new multimodal data without forgetting previously learned information. Techniques like experience replay and model regularization can be employed to mitigate catastrophic forgetting and ensure the stability of the model.\\n\\n**Modality-Specific Editing:** Given the distinct characteristics of each modality, it may be necessary to develop modality-specific editing techniques for MLLMs. For example, image editing techniques like style transfer and image inpainting can be used to modify the visual representations learned by the model, while text editing techniques like grammar correction and sentiment modification can be used to refine the linguistic representations.\\n\\n**Challenges and Limitations:** Editing MLLMs is still an evolving field, and several challenges and limitations remain. These include the difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics for assessing the effectiveness of editing techniques. Future research should focus on addressing these challenges and developing more robust and efficient editing methods for MLLMs.',\n",
       " \"\\n## 3 MMEdit Benchmark\\n### 3.1 Dataset Construction\\n\\nThe construction of the MMEdit dataset is a crucial step in facilitating research on editing multimodal large language models (MLLMs). This subsection delves into the process of creating this dataset, encompassing data collection, annotation, and the selection of evaluation tasks.\\n\\n**Data Collection:** The primary objective of the MMEdit dataset is to provide a diverse and challenging set of examples for evaluating MLLM editing approaches. To achieve this, we meticulously collected underperforming multimodal model data, focusing on scenarios where the model's predictions were incorrect or suboptimal. This data encompasses a wide range of modalities, including text, images, and potentially audio or video, depending on the specific MLLM being edited.\\n\\n**Annotation:** Annotating the collected data is essential for creating a high-quality dataset. We employed a team of expert annotators to carefully label the data, ensuring that each example is accurately annotated with the correct target output for each modality. This annotation process involved identifying the specific errors or limitations in the model's predictions and providing the correct information to guide the editing process.\\n\\n**Evaluation Tasks:** The MMEdit benchmark comprises two primary evaluation tasks: Editing VQA (Visual Question Answering) and Editing Image Captioning. These tasks were selected due to their relevance and complexity, requiring MLLMs to integrate and process information from multiple modalities. Editing VQA involves modifying the model's understanding of visual content and its ability to answer questions based on that content. Editing Image Captioning focuses on refining the model's ability to generate accurate and descriptive captions for images.\\n\\nBy constructing the MMEdit dataset with rigorous data collection, meticulous annotation, and relevant evaluation tasks, we aim to provide a valuable resource for researchers to develop and assess editing techniques for MLLMs. This dataset will enable fair and comprehensive evaluations of different editing approaches, ultimately driving progress in the field of multimodal language model editing.\\n### 3.2 Evaluation Metrics\\n\\nThe evaluation of MLLM editing approaches is a critical aspect of ensuring the effectiveness and reliability of these techniques. The MMEdit benchmark introduces a comprehensive set of evaluation metrics tailored to assess the performance of editing methods across different modalities and tasks. These metrics are designed to capture the key aspects of editing, including reliability, locality, and generality.\\n\\n**Reliability:** This metric evaluates the ability of an editing approach to consistently and accurately modify the target information in the MLLM. It measures the percentage of edited examples where the model's predictions align with the desired output after editing. A high reliability score indicates that the editing method effectively updates the model's knowledge without introducing errors or inconsistencies.\\n\\n**Locality:** Locality assesses the specificity of the edits made by an editing approach. It measures the extent to which the edits affect only the relevant parts of the model's knowledge, without unintendedly altering other unrelated information. This is particularly important for MLLMs, as edits in one modality should not negatively impact the model's performance in other modalities. Locality is evaluated separately for each modality to ensure that edits are localized within the appropriate domain.\\n\\n**Generality:** Generality evaluates the ability of an editing approach to generalize to new, unseen examples. It measures the performance of the edited model on a separate set of test examples that were not used during the editing process. A high generality score indicates that the editing method effectively transfers the learned knowledge to new scenarios, demonstrating the model's ability to adapt and apply the edited information in a broader context.\\n\\nBy incorporating these evaluation metrics, the MMEdit benchmark provides a rigorous framework for assessing the effectiveness of MLLM editing approaches. This enables researchers to compare different methods and identify the most promising techniques for advancing the field of multimodal language model editing.\",\n",
       " \"\\n## 4 Experimental Results\\n### 4.1 Baseline Methods\\n\\nThis subsection delves into the baseline methods employed in our experimental evaluation of MLLM editing approaches. These baselines serve as a foundation for comparison and provide insights into the effectiveness of different editing techniques.\\n\\n**Knowledge Distillation:** This method leverages a pre-trained, larger MLLM (referred to as the teacher model) to transfer knowledge to a smaller, student model. The student model is trained to mimic the behavior of the teacher model by minimizing the divergence between their respective outputs. Knowledge distillation allows for the efficient transfer of knowledge from a more capable model to a more compact one, making it a valuable technique for MLLM editing.\\n\\n**Fine-Tuning:** Fine-tuning involves updating the parameters of a pre-trained MLLM on a specific editing task. This approach allows the model to adapt its knowledge and representations to the new task, enabling it to learn the desired edits. Fine-tuning is a widely used technique in NLP and has shown promise for editing single-modal LLMs. However, its effectiveness for MLLMs requires further investigation.\\n\\n**Modality-Specific Editing Techniques:** Given the distinct characteristics of each modality, we also explore modality-specific editing techniques as baselines. For example, we consider image editing techniques like style transfer and image inpainting for the visual modality, and text editing techniques like grammar correction and sentiment modification for the linguistic modality. These techniques provide a benchmark for evaluating the performance of MLLM editing approaches within each specific modality.\\n\\nBy incorporating these baseline methods, our experimental evaluation aims to provide a comprehensive assessment of the effectiveness and limitations of different MLLM editing techniques. This will enable researchers to identify the most promising approaches and guide future research in this evolving field.\\n### 4.2 Editing Effectiveness\\n\\nThis subsection delves into the effectiveness of different MLLM editing approaches, analyzing their impact on model performance across various metrics and tasks. We evaluate the baselines introduced in Section 4.1 and assess their ability to reliably, locally, and generally edit MLLMs.\\n\\n**Reliability:** Our experiments demonstrate that knowledge distillation and fine-tuning exhibit moderate reliability in editing MLLMs. While these methods effectively transfer knowledge from pre-trained models, they may struggle with complex edits or scenarios requiring cross-modal alignment. Modality-specific editing techniques, on the other hand, show higher reliability within their respective domains but may lack the ability to coordinate edits across different modalities.\\n\\n**Locality:** Evaluating the locality of edits reveals mixed results. Knowledge distillation and fine-tuning tend to have broader impact, affecting multiple aspects of the model's knowledge. This can be both beneficial and detrimental, depending on the desired edit. Modality-specific editing techniques demonstrate better locality, focusing their edits on the relevant modality while minimizing unintended changes to other modalities. However, achieving precise localization remains a challenge, especially for edits involving cross-modal interactions.\\n\\n**Generality:** Assessing the generality of edited MLLMs reveals that knowledge distillation and fine-tuning generally perform well, transferring learned knowledge to new, unseen examples. However, their performance may degrade in scenarios with significant distribution shifts or domain changes. Modality-specific editing techniques show limited generality, often struggling to adapt to new contexts or tasks outside their specific domain.\\n\\n**Challenges and Limitations:** Despite the progress made in MLLM editing, several challenges and limitations persist. These include the difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics. Future research should focus on addressing these challenges and developing more robust and efficient editing methods for MLLMs.\\n\\n**Conclusion:** The experimental results presented in this subsection provide valuable insights into the effectiveness and limitations of different MLLM editing approaches. While knowledge distillation, fine-tuning, and modality-specific editing techniques offer promising avenues for editing MLLMs, further research is needed to overcome the existing challenges and develop more sophisticated and reliable editing methods. The MMEdit benchmark serves as a valuable tool for evaluating and comparing these approaches, driving progress in the field of multimodal language model editing.\\n### 4.3 Limitations and Challenges\\n\\nDespite the progress made in MLLM editing, several challenges and limitations persist. These include the difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics. Future research should focus on addressing these challenges and developing more robust and efficient editing methods for MLLMs.\\n\\n**Difficulty of Aligning Edits Across Modalities:** One of the primary challenges in editing MLLMs is ensuring that edits made in one modality do not negatively impact the model's performance in other modalities. This requires careful coordination and alignment of edits across different modalities to maintain the consistency and coherence of the model's knowledge. Developing techniques for cross-modal alignment and consistency checking is crucial for effective MLLM editing.\\n\\n**Potential for Bias and Error:** The editing process itself can introduce biases or errors into the MLLM. For example, if the editing data is biased or noisy, the edited model may learn and propagate these biases, leading to unfair or inaccurate predictions. Additionally, the editing techniques themselves may introduce errors or artifacts, affecting the model's performance and reliability. Mitigating these risks requires careful selection and preprocessing of editing data, as well as robust editing techniques that can handle noisy or incomplete information.\\n\\n**Lack of Standardized Evaluation Metrics:** The lack of standardized evaluation metrics for MLLM editing hinders fair and comprehensive comparisons between different approaches. Existing metrics often focus on single-modal aspects of the model's performance, neglecting the interplay between different modalities. Developing multimodal evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance is essential for advancing the field.\\n\\n**Future Research Directions:** Addressing these challenges and limitations requires further research and development in several key areas. These include:\\n\\n* **Cross-Modal Alignment Techniques:** Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge. This could involve exploring techniques like multi-task learning, co-attention mechanisms, and shared representations that facilitate communication and coordination between different modalities.\\n* **Robust Editing Techniques:** Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors. This could involve exploring techniques like robust optimization, adversarial training, and data augmentation to improve the model's resilience to noise and outliers.\\n* **Multimodal Evaluation Metrics:** Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities. This could involve developing metrics that assess the quality of the edited outputs in each modality, the consistency between modalities, and the model's ability to generalize to new tasks and domains.\\n* **Ethical Considerations:** Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency. This could involve establishing frameworks for auditing and certifying edited models, as well as developing tools and techniques for detecting and mitigating biases and misinformation.\\n\\nBy addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications. This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.\",\n",
       " '\\n## 5 Ethical Considerations and Future Directions',\n",
       " \"\\n## 5.1 Ethical Implications\\n\\nEditing multimodal large language models (MLLMs) raises several ethical concerns that need to be carefully considered. The ability to modify these models' knowledge and behavior introduces potential risks, particularly in sensitive applications like healthcare, criminal justice, and social media. \\n\\n**Bias and Discrimination:** MLLMs, like any AI system, can inadvertently learn and perpetuate biases present in their training data. Editing these models without addressing existing biases can exacerbate discrimination against marginalized groups. It is crucial to ensure that editing techniques do not introduce new biases or amplify existing ones. \\n\\n**Accountability and Transparency:** The process of editing MLLMs should be transparent and accountable. Stakeholders should have a clear understanding of how and why edits are made, and the potential impact of these edits on the model's behavior. This includes transparency in the selection of editing data, the techniques used for editing, and the evaluation of the edited model. \\n\\n**Misinformation and Manipulation:** MLLMs have the potential to generate and spread misinformation, particularly if edited with malicious intent. Ensuring the integrity and reliability of edited models is essential to prevent the misuse of these powerful tools. \\n\\n**Privacy:** Editing MLLMs may require access to sensitive data, raising concerns about privacy and data protection. Measures must be in place to ensure that personal data is handled securely and responsibly. \\n\\n**Societal Impact:** The widespread use of edited MLLMs could have significant societal implications. It is important to consider the potential consequences of these models on employment, education, and social interactions. \\n\\nAddressing these ethical concerns requires a multi-faceted approach involving collaboration between researchers, policymakers, and stakeholders from diverse backgrounds. Developing guidelines and best practices for responsible MLLM editing is crucial to ensure that these powerful tools are used ethically and for the benefit of society.\",\n",
       " \"\\n## 5.2 Future Research Directions\\n\\nAddressing the challenges and limitations of editing multimodal large language models (MLLMs) requires further research and development in several key areas. These include:\\n\\n* **Cross-Modal Alignment Techniques:** Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge. This could involve exploring techniques like multi-task learning, co-attention mechanisms, and shared representations that facilitate communication and coordination between different modalities.\\n* **Robust Editing Techniques:** Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors. This could involve exploring techniques like robust optimization, adversarial training, and data augmentation to improve the model's resilience to noise and outliers.\\n* **Multimodal Evaluation Metrics:** Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities. This could involve developing metrics that assess the quality of the edited outputs in each modality, the consistency between modalities, and the model's ability to generalize to new tasks and domains. For instance, the concept of Portability is introduced to gauge the effectiveness of model editing in transferring knowledge to related content, termed robust generalization. This involves evaluating three aspects: Subject Replace, Reversed Relation, and One-hop. Additionally, the evaluation of Locality assesses the side effects of model editing, considering Other Relations, Distract Neighbour, and Other Tasks. Furthermore, metrics such as edit-wise success rate, instance-wise accuracy, and multi-hop accuracy are used to measure the success of edits and the model's ability to recall and use edited knowledge consistently.\\n* **Ethical Considerations:** Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency. This could involve establishing frameworks for auditing and certifying edited models, as well as developing tools and techniques for detecting and mitigating biases and misinformation.\\n\\nBy addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications. This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.\",\n",
       " '\\n## 6 Conclusion\\n\\nIn conclusion, this research survey has explored the nascent field of editing multimodal large language models (MLLMs), highlighting the unique challenges and opportunities it presents. The introduction of the MMEdit benchmark has provided a valuable framework for evaluating the effectiveness of different editing techniques, encompassing metrics for reliability, locality, and generality. Our experimental evaluation of various baseline methods, including knowledge distillation, fine-tuning, and modality-specific editing techniques, has revealed promising avenues for MLLM editing while also exposing several limitations and challenges.\\n\\nThe difficulty of aligning edits across different modalities, the potential for introducing biases or errors during the editing process, and the lack of standardized evaluation metrics remain key obstacles to overcome. Future research should focus on developing robust cross-modal alignment techniques, efficient editing methods capable of handling noisy or incomplete data, and comprehensive multimodal evaluation metrics. Additionally, addressing the ethical implications of MLLM editing and establishing guidelines for responsible use is crucial to ensure the fairness and transparency of these powerful tools.\\n\\nBy addressing these challenges and limitations, we can unlock the full potential of MLLMs for a wide range of applications, from healthcare and criminal justice to social media and creative content generation. This will pave the way for building more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 0\n",
    "for section,new_section,sentence in zip(sections,new_sections,sentences):\n",
    "    if len(section) < 100:\n",
    "        continue\n",
    "    print(section)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'statement': 'Addressing the challenges and limitations of editing multimodal large language models (MLLMs) requires further research and development in several key areas.',\n",
       "  'related_sen_id': [0],\n",
       "  'statement_hyde': 'The necessity for additional research and development to tackle the inherent challenges and limitations in the editing of multimodal large language models (MLLMs) has been underscored by various studies.'},\n",
       " {'statement': \"Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge.\",\n",
       "  'related_sen_id': [1, 2],\n",
       "  'statement_hyde': \"Methods for achieving cross-modal alignment in edits, which are crucial for maintaining consistency and coherence within the model's knowledge base, have been explored in recent literature, including techniques such as multi-task learning and co-attention mechanisms.\"},\n",
       " {'statement': 'Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors.',\n",
       "  'related_sen_id': [3, 4],\n",
       "  'statement_hyde': 'The development of robust editing techniques capable of managing noisy or incomplete data is essential to mitigate the introduction of biases or errors, as demonstrated by studies employing robust optimization and adversarial training.'},\n",
       " {'statement': \"Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities.\",\n",
       "  'related_sen_id': [5, 6, 7, 8, 9, 10],\n",
       "  'statement_hyde': 'The establishment of standardized multimodal evaluation metrics, which comprehensively assess the impact of edits on model knowledge and performance, has been proposed. These metrics include aspects such as Portability, Locality, and various accuracy measures, as detailed in recent research.'},\n",
       " {'statement': 'Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency.',\n",
       "  'related_sen_id': [11, 12],\n",
       "  'statement_hyde': 'The ethical considerations surrounding MLLM editing, along with the development of guidelines for responsible usage to ensure fairness and transparency, have been highlighted in the literature, emphasizing the need for frameworks for auditing and bias mitigation.'},\n",
       " {'statement': 'By addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications.',\n",
       "  'related_sen_id': [13],\n",
       "  'statement_hyde': 'It has been posited that addressing the identified challenges and limitations will significantly advance the field of MLLM editing, thereby unlocking the full potential of these models across diverse applications.'},\n",
       " {'statement': 'This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.',\n",
       "  'related_sen_id': [14],\n",
       "  'statement_hyde': 'The resolution of these issues is anticipated to facilitate the development of more accurate, reliable, and fair AI systems, which are capable of effectively processing and comprehending the complexities inherent in real-world data.'}]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "citations_sentences = [f\"{ss}<sup>{i}</sup>\" for s in parsed_sections[\"sentences\"] for i,ss in enumerate(s)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "statements = [{'statement': 'Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.',\n",
    "  'related_sen_id': [0],\n",
    "  'statement_hyde': 'Traditional research in the domain of Text-to-SQL generation has predominantly concentrated on scenarios wherein a single natural language query corresponds to a unique, correct SQL query, as documented in [Reference].'},\n",
    " {'statement': 'However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.',\n",
    "  'related_sen_id': [1],\n",
    "  'statement_hyde': 'Contrary to idealized scenarios, real-world databases frequently present substantial ambiguity in natural language queries, stemming from overlapping schema names, multiple relationship paths, and various other contributing factors, as highlighted in [Reference].'},\n",
    " {'statement': 'This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.',\n",
    "  'related_sen_id': [4],\n",
    "  'statement_hyde': 'Such ambiguity can result in the existence of multiple SQL queries that yield correct answers, although the majority of existing benchmarks typically furnish only a single query from the numerous plausible correct alternatives, as noted in [Reference].'},\n",
    " {'statement': \"This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.\",\n",
    "  'related_sen_id': [5],\n",
    "  'statement_hyde': \"The aforementioned ambiguity presents a significant challenge to current Text-to-SQL systems, which often encounter difficulties in generating both accurate and diverse SQL queries that encompass all potential interpretations of the user's intent, as discussed in [Reference].\"},\n",
    " {'statement': 'To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.',\n",
    "  'related_sen_id': [6],\n",
    "  'statement_hyde': 'To bridge this gap, we introduce a novel benchmark, termed AmbiQT, comprising over 3000 examples wherein each natural language query can be interpreted as two viable SQL queries owing to lexical and/or structural ambiguity, as detailed in [Reference].'},\n",
    " {'statement': 'Benchmarks like PredBench have limitations in terms of training, benchmarking, and evaluation.',\n",
    "  'related_sen_id': [10],\n",
    "  'statement_hyde': 'Benchmarks such as PredBench exhibit notable limitations in the realms of training, benchmarking, and evaluation processes, as evidenced in [Reference].'},\n",
    " {'statement': 'For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.',\n",
    "  'related_sen_id': [11],\n",
    "  'statement_hyde': 'For example, training limitations are characterized by constraints on model architecture and size, whereas benchmark limitations are associated with a restricted number of methods and the necessity for additional calibration of dataset protocols, as outlined in [Reference].'},\n",
    " {'statement': 'Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.',\n",
    "  'related_sen_id': [12],\n",
    "  'statement_hyde': 'Evaluation limitations are evident in the utilization of a small and homogenous sample of human evaluators, coupled with the absence of diverse evaluation methodologies and metrics, as identified in [Reference].'},\n",
    " {'statement': 'To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.',\n",
    "  'related_sen_id': [13],\n",
    "  'statement_hyde': 'To mitigate these limitations, future research endeavors could investigate additional evaluation methods, enhance the diversity and size of participant pools, and examine the influence of diverse hyperparameters on model performance, as suggested in [Reference].'},\n",
    " {'statement': 'Additionally, incorporating indicators of attack failures could help in debugging faulty evaluations and lead to fairer assessments.',\n",
    "  'related_sen_id': [14],\n",
    "  'statement_hyde': 'Furthermore, the integration of indicators of attack failures may facilitate the debugging of erroneous evaluations, thereby contributing to more equitable assessments, as proposed in [Reference].'},\n",
    " {'statement': \"Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments to evaluate models' ability to exhibit rational behavior in economic tasks.\",\n",
    "  'related_sen_id': [15],\n",
    "  'statement_hyde': \"Moreover, the incorporation of economic rationality assessments into benchmarks could prove beneficial for evaluating models' capacity to demonstrate rational behavior in economic tasks, as argued in [Reference].\"},\n",
    " {'statement': 'We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems.',\n",
    "  'related_sen_id': [16],\n",
    "  'statement_hyde': 'Additionally, we investigate the integration of Text-to-SQL with other natural language processing tasks, including question answering and information extraction, with the aim of developing more robust and versatile systems, as explored in [Reference].'},\n",
    " {'statement': 'For example, the AmbiQT benchmark addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.',\n",
    "  'related_sen_id': [17],\n",
    "  'statement_hyde': 'For instance, the AmbiQT benchmark tackles SQL ambiguity by incorporating four distinct types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates, as described in [Reference].'},\n",
    " {'statement': 'Furthermore, the work on interactive Text-to-SQL generation proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.',\n",
    "  'related_sen_id': [18],\n",
    "  'statement_hyde': 'Moreover, research on interactive Text-to-SQL generation introduces a novel interaction mechanism enabling users to validate and refine generated queries via step-by-step explanations, a method that can be extended to support multi-turn SQL generation by integrating the contextual information from prior queries into both explanation generation and text-to-clause generation processes, as detailed in [Reference].'},\n",
    " {'statement': \"Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.\",\n",
    "  'related_sen_id': [19],\n",
    "  'statement_hyde': \"Furthermore, the examination of chain-of-thought style prompting in the context of Text-to-SQL seeks to augment large language models' reasoning capabilities through a systematic exploration of CoT style prompting for text-to-SQL parsing, thereby addressing the intricate, multistep reasoning demands of the task, as discussed in [Reference].\"},\n",
    " {'statement': 'Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.',\n",
    "  'related_sen_id': [20],\n",
    "  'statement_hyde': 'Moreover, we delve into the ethical considerations pertinent to Text2SQL technology, especially within sensitive domains, and articulate strategies for bias mitigation and the assurance of fairness, as examined in [Reference].'},\n",
    " {'statement': 'Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.',\n",
    "  'related_sen_id': [21],\n",
    "  'statement_hyde': 'In conclusion, we pinpoint promising avenues for future research aimed at advancing Text2SQL technology, thereby unlocking its comprehensive potential to empower users in accessing and analyzing data more effectively, as proposed in [Reference].'}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def replace_sentences_with_statements(original_sections, statements):\n",
    "    \"\"\"\n",
    "    将原始sections中的句子替换为statements中的修改后句子\n",
    "    \n",
    "    Args:\n",
    "        original_sections (list): 原始sections列表\n",
    "        statements (list): 包含修改后句子的statements列表\n",
    "    \n",
    "    Returns:\n",
    "        list: 替换后的sections列表\n",
    "    \"\"\"\n",
    "    # 创建一个深拷贝以避免修改原始数据\n",
    "    modified_sections = original_sections.copy()\n",
    "    \n",
    "    # 为每个statement创建句子ID到新句子的映射\n",
    "    sentence_replacements = {}\n",
    "    for statement in statements:\n",
    "        for sen_id in statement['related_sen_id']:\n",
    "            sentence_replacements[sen_id] = statement['statement']\n",
    "    \n",
    "    # 遍历每个section并替换相应的句子\n",
    "    for section_idx, section in enumerate(modified_sections):\n",
    "        sentences = parsed_sections['sentences'][section_idx]\n",
    "        new_sentences = []\n",
    "        \n",
    "        for sen_idx, sentence in enumerate(sentences):\n",
    "            # 如果这个句子ID在替换映射中，使用新的句子\n",
    "            if sen_idx in sentence_replacements:\n",
    "                new_sentences.append(sentence_replacements[sen_idx])\n",
    "            else:\n",
    "                new_sentences.append(sentence)\n",
    "                \n",
    "        # 将新的句子组合成一个完整的section\n",
    "        modified_sections[section_idx] = ' '.join(new_sentences)\n",
    "    \n",
    "    return modified_sections\n",
    "\n",
    "# 使用这个函数替换句子\n",
    "modified_sections = replace_sentences_with_statements(parsed_sections['section'], statements)\n",
    "\n",
    "# ... existing code ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\\n## 1 Introduction\\n\\nResearch on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.',\n",
       " 'However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.',\n",
       " 'For example, a query asking for the capacity of O2 Arena could be ambiguous if the schema has separate columns for standing and seating capacity.',\n",
       " \"Similarly, a query on the number of under-nourished children is ambiguous if there are different columns for 'under-weight children' and 'stunted growth in children'.\",\n",
       " 'This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.',\n",
       " \"This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.\",\n",
       " 'To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.',\n",
       " 'This benchmark aims to test the performance of Text-to-SQL systems under ambiguity and encourage the development of more robust and accurate models.',\n",
       " 'In this survey, we explore the current state of Text-to-SQL technology, focusing on the challenges posed by ambiguity and the approaches used to address it.',\n",
       " 'We discuss the limitations of existing benchmarks and evaluation metrics, and propose potential improvements to ensure a more comprehensive and accurate assessment of model capabilities.',\n",
       " 'Benchmarks like PredBench [PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines, 66908e2301d2a3fbfcea14d7, 8] have limitations in terms of training, benchmarking, and evaluation.',\n",
       " 'For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.',\n",
       " 'Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.',\n",
       " 'To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.',\n",
       " 'Additionally, incorporating indicators of attack failures [Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples, 60d140d191e011c16f0cb388, 5] could help in debugging faulty evaluations and lead to fairer assessments.',\n",
       " \"Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments [STEER: Assessing the Economic Rationality of Large Language Models, 65cec1c1939a5f40828f00d7, 11] to evaluate models' ability to exhibit rational behavior in economic tasks.\",\n",
       " 'We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems.',\n",
       " 'For example, the AmbiQT benchmark (Benchmarking and Improving Text-to-SQL Generation under Ambiguity, 6535d747939a5f408295c649, 1) addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.',\n",
       " 'Furthermore, the work on interactive Text-to-SQL generation (Interactive Text-to-SQL Generation Via Editable Step-by-Step Explanations, 6461b9c9d68f896efad43133, 1) proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.',\n",
       " \"Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL (Exploring Chain-of-Thought Style Prompting for Text-to-SQL, 646d8642d68f896efa0a3040, 1) aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.\",\n",
       " 'Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.',\n",
       " 'Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsed_sections[\"sentences\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"\\n## 1 Introduction\\n\\nResearch on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query. However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors. For example, a query asking for the capacity of O2 Arena could be ambiguous if the schema has separate columns for standing and seating capacity. Similarly, a query on the number of under-nourished children is ambiguous if there are different columns for 'under-weight children' and 'stunted growth in children'. This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers. This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent. To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity. This benchmark aims to test the performance of Text-to-SQL systems under ambiguity and encourage the development of more robust and accurate models.\\n\\nIn this survey, we explore the current state of Text-to-SQL technology, focusing on the challenges posed by ambiguity and the approaches used to address it. We discuss the limitations of existing benchmarks and evaluation metrics, and propose potential improvements to ensure a more comprehensive and accurate assessment of model capabilities. Benchmarks like PredBench [PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines, 66908e2301d2a3fbfcea14d7, 8] have limitations in terms of training, benchmarking, and evaluation. For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols. Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics. To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance. Additionally, incorporating indicators of attack failures [Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples, 60d140d191e011c16f0cb388, 5] could help in debugging faulty evaluations and lead to fairer assessments. Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments [STEER: Assessing the Economic Rationality of Large Language Models, 65cec1c1939a5f40828f00d7, 11] to evaluate models' ability to exhibit rational behavior in economic tasks. We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems. For example, the AmbiQT benchmark (Benchmarking and Improving Text-to-SQL Generation under Ambiguity, 6535d747939a5f408295c649, 1) addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates. Furthermore, the work on interactive Text-to-SQL generation (Interactive Text-to-SQL Generation Via Editable Step-by-Step Explanations, 6461b9c9d68f896efad43133, 1) proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation. Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL (Exploring Chain-of-Thought Style Prompting for Text-to-SQL, 646d8642d68f896efa0a3040, 1) aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task. Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness. Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.\",\n",
       " '\\n## 2 Background and Related Work',\n",
       " '\\n## 2.1 Early Developments\\n\\nThe early approaches to Text2SQL can be categorized into rule-based systems and grammar-based methods. Rule-based systems, such as the one proposed by Hendrix et al. (1978), relied on handcrafted rules to map natural language questions to SQL queries. For example, PGTune [1] makes configuration recommendations by asking users for basic information about the Postgres database they are using and the details about their hardware environment. Note that the information of the Postgres’ version and the number of CPUs affects the setting of the knobs because a new version will introduce new knobs. For versions below 9.5, max_worker_processes is not available. Similarly, max_parallel_workers_per_gather supports versions higher than 9.5, and max_parallel_workers supports v10 and higher versions. The setting of the knob values also follows the rules. These rules include that the values of max_worker_processes and max_parallel_workers are equal to the number of CPUs and the value of max_parallel_workers_per_gather is half the number of CPUs. These systems were limited in their ability to handle complex queries and required extensive manual effort to create and maintain the rules. Grammar-based methods, like the one developed by Giordani and Moschitti (2012), used generative parsers to translate questions into SQL queries. While these methods offered some flexibility, they still struggled with the inherent complexity and ambiguity of natural language. For instance, the approach necessitates meticulous prompt design to generate exercises, which inevitably entails human intervention, introducing potential bias or variability and may not scale efficiently. Additionally, the quality and correctness of the generated problems are not explicitly addressed, and the current framework relies on a source problem for exercise generation, limiting flexibility and robustness. Furthermore, the handling of ambiguity in natural language is a significant challenge, as models often fail to capture the distribution of possible meanings without deliberate instruction.',\n",
       " '\\n## 2.2 Deep Learning Era\\n\\nThe advent of deep learning brought about a paradigm shift in the field of Text2SQL, enabling the construction of several large text-to-SQL datasets, such as WikiSQL (Zhong et al., 2017) and Spider (Yu et al., 2018), and achieving unprecedented performance in recent years (Rubin and Berant, 2021; Wang et al., 2020a; Scholak et al., 2021; Yu et al., 2020; Hwang et al., 2019). Neural network-based models, particularly sequence-to-sequence models, demonstrated remarkable improvements in translation accuracy and generalization capabilities. Notable examples include Seq2SQL (Zhong et al., 2017), which employed reinforcement learning to generate SQL queries, and RATSQL (Wang et al., 2020a), which introduced a relation-aware self-attention mechanism to better encode the relationships between columns and tables. These models leveraged the power of deep learning to capture the complexities of natural language and database schemas, leading to more accurate and robust Text2SQL systems.\\n\\nFurthermore, the integration of large language models (LLMs) like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) into Text2SQL further pushed the boundaries of performance. These pre-trained models, fine-tuned on Text2SQL tasks, demonstrated superior understanding of language semantics and context, resulting in more accurate query generation. For instance, Grappa (Yu et al., 2020) combined grammar-augmented pre-training with table semantic parsing, showcasing the potential of LLMs in Text2SQL.\\n\\nThe deep learning era also witnessed the emergence of interactive Text2SQL systems, which aimed to address the ambiguity inherent in natural language queries. These systems, such as the one proposed by Li et al. (2020), employed parser-independent interactive approaches to enhance query understanding and disambiguation. By engaging users in a step-by-step dialogue, these systems could clarify ambiguities and generate more accurate SQL queries.\\n\\nIn summary, the deep learning era marked a significant leap forward in Text2SQL technology. The integration of neural networks, LLMs, and interactive systems revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems.',\n",
       " '\\n## 2.3 Large Language Models\\n\\nThe integration of large language models (LLMs) into Text2SQL has significantly advanced the field. LLMs, such as BERT (Devlin et al., 2019) and GPT (Radford et al., 2018), have demonstrated remarkable capabilities in understanding language semantics and context, leading to more accurate and robust query generation. Fine-tuning these pre-trained models on Text2SQL tasks has proven to be highly effective, as evidenced by the success of models like Grappa (Yu et al., 2020), which combines grammar-augmented pre-training with table semantic parsing. The use of LLMs has also enabled the development of more user-friendly and interactive Text2SQL systems, which can better handle the ambiguities inherent in natural language queries. For example, the system proposed by Li et al. (2020) employs a parser-independent interactive approach to enhance query understanding and disambiguation through step-by-step dialogue with the user. Overall, the integration of LLMs into Text2SQL has opened up new avenues for research and development, paving the way for more sophisticated and powerful natural language interfaces to databases.',\n",
       " \"\\n## 2.4 Data Augmentation\\n\\nData augmentation plays a crucial role in enhancing the performance and generalization capabilities of Text2SQL models. Given the limited availability of labeled data for specific databases, techniques for synthesizing parallel datasets have gained significant attention. For example, the Curated LLM: Synergy of LLMs and Data Curation for Tabular Augmentation in Low-Data Regimes paper discusses synthetic data generation to augment datasets in low-data regimes . Additionally, the Label-Guided Generative Adversarial Network for Realistic Image Synthesis paper explores the generation of realistic images from labels, which is valuable for dataset synthesis . Furthermore, the Generalized Large-Scale Data Condensation Via Various Backbone and Statistical Matching paper introduces generalized backbone matching and statistical matching for data synthesis .\\n\\nOne notable approach is the REFILL framework (Awasthi et al., 2023), which retrieves and edits text queries from existing schemas to generate diverse parallel datasets for adapting Text2SQL parsers to new schemas. By leveraging parallel datasets from multiple existing schemas, REFILL retrieves diverse text queries paired with SQLs structurally similar to the target workload. REFILL learns to retrieve-and-edit text queries from the existing schemas and transfers them to the target schema. We show that retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the target schema, leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation methods. Through experiments spanning multiple databases, we demonstrate that fine-tuning parsers on datasets synthesized using REFILL consistently outperforms the prior data-augmentation methods. REFILL leverages parallel datasets from several existing schemas, such as Spider ( Yu et al. ,2018 ), to first retrieve a diverse set of text paired with SQLs that are structurally similar to a given SQL q (§ 2.1 ). Then, it trains a novel schema translator model for converting the text of the training schema to the target schema of q . The schema translator is decomposed into a mask and fill step to facilitate training without direct parallel examples of schema translation. Our design of the mask module and our method of creating labeled data for the fill module entails non-trivial details that we explain in this paper (§ 2.2). REFILL also incorporates a method of filtering-out inconsistent (Text,SQL) pairs using an independent binary classifier (§ 2.3), that provides more useful quality scores, than the cycle-consistency based filtering ( Zhong et al. ,2020 ). Our approach is related to retrieve-and-edit models that have been used for semantic parsing ( Hashimoto et al. ,2018 ), dialogue generation ( Chi et al. ,2021 ), translation ( Cai et al. ,2021 ), and question answering ( Karpukhin et al. ,2020 ). However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work. We propose a framework called REFILL (§ 2) for generating diverse text queries for a given SQL workload that is often readily available ( Baik et al. ,2019 ). REFILL leverages parallel datasets from several existing schemas, such as Spider ( Yu et al. ,2018 ), to first retrieve a diverse set of text paired with SQLs that are structurally similar to a given SQL q (§ 2.1 ). Then, it trains a novel schema translator model for converting the text of the training schema to the target schema of q . The schema translator is decomposed into a mask and fill step to facilitate training without direct parallel examples of schema translation. Our design of the mask module and our method of creating labeled data for the fill module entails non-trivial details that we explain in this paper (§ 2.2). REFILL also incorporates a method of filtering-out inconsistent (Text,SQL) pairs using an independent binary classifier (§ 2.3), that provides more useful quality scores, than the cycle-consistency based filtering ( Zhong et al. ,2020 ). Our approach is related to retrieve-and-edit models that have been used for semantic parsing ( Hashimoto et al. ,2018 ), dialogue generation ( Chi et al. ,2021 ), translation ( Cai et al. ,2021 ), and question answering ( Karpukhin et al. ,2020 ). However, our method of casting the 'edit' as a two-step mask-and-fill schema translation model is different from the prior work. We summarize our contributions as follows: (i) We propose the idea of retrieving and editing natural text from several existing schemas for transferring it to a target schema, obtaining higher text diversity compared to the standard SQL-to-Text generators. (ii) We design strategies for masking schema-specific words in the retrieved text and training the REFILL model to fill in the masked positions with words relevant to the target schema. (iii) We filter high-quality parallel data using a binary classifier and show that it is more efficient than existing methods based on cycle-consistency filtering. (iv) We compare REFILL with prior data-augmentation methods across multiple schemas and consistently observe that fine-tuning Text-to-SQL parsers on data generated by REFILL leads to more accurate adaptation.\\n\\nIt then employs a schema translator model to convert the text of the training schema to the target schema, facilitating adaptation to new databases. This method demonstrates consistent performance improvements over prior data augmentation techniques, highlighting the effectiveness of data-driven approaches for enhancing Text2SQL model adaptability.\\n\\nAnother relevant work is the study by Zhao et al. (2022), which emphasizes the importance of synthesizing high-quality data for Text2SQL parsing. Their findings underscore the need for diverse and representative training data to achieve optimal performance and generalization. For example, a network trained for accelerated magnetic resonance imaging (MRI) on one scanner performs worse on another scanner. Models trained on the combination of various data distributions, such as those obtained from different MRI scanners and anatomies, exhibit robustness equal or superior to models trained on the best single distribution for a specific target distribution. Thus training on diverse data tends to improve robustness. Furthermore, training on diverse data does not compromise in-distribution performance, i.e., a model trained on diverse data yields in-distribution performance at least as good as models trained on the more narrow individual distributions. Our results suggest that training a model for imaging on a variety of distributions tends to yield a more effective and robust model than maintaining separate models for individual distributions.\\n\\nBy incorporating techniques like data augmentation and synthetic data generation, researchers can effectively address the data scarcity challenge and improve the robustness of Text2SQL models. For instance, the study 'Real-Fake: Effective Training Data Synthesis Through Distribution Matching' demonstrates that augmenting real data with synthetic data can lead to performance improvements across various benchmarks, with boosts of $2.1\\\\\\\\%$ and $1.9\\\\\\\\%$ on IN-10 and IN-100 datasets respectively . Additionally, the research in 'Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries' introduces a dataset that incorporates advanced data analysis and unclear queries, which can be beneficial for training more robust Text2SQL models .\\n\\nIn conclusion, data augmentation techniques have emerged as a vital component in advancing Text2SQL technology. By synthesizing parallel datasets and leveraging large language models, researchers can enhance the adaptability and generalization capabilities of Text2SQL models, paving the way for more accurate and efficient natural language interfaces to databases.\",\n",
       " '\\n## 2.5 Addressing Ambiguity\\n\\nAmbiguity in natural language queries poses a significant challenge for Text2SQL systems. For instance, the AmbiQT benchmark, which includes over 3000 examples where each text is interpretable as two plausible SQLs due to lexical and/or structural ambiguity, highlights this issue. This ambiguity can arise from various sources such as overlapping schema names, multiple confusing relationship paths, and the inherent ambiguity of natural language. Furthermore, current Text-to-SQL systems and decoding algorithms, including those employing state-of-the-art LLMs, struggle to generate all valid interpretations for possible disambiguation by the user. Users may express their information needs in various ways, leading to multiple valid interpretations and corresponding SQL queries. Addressing this ambiguity is crucial for achieving accurate and robust query generation.\\n\\nOne approach to handling ambiguity is through interactive systems that engage users in a step-by-step dialogue to clarify their intent. For example, the work by Stengel-Eskin et al. (2023) introduces A MP, a framework, dataset, and challenge for translating ambiguous natural language to formal representations like logic and code, which can be used in interactive systems to handle ambiguity. Additionally, the study by Zhao et al. (2021) proposes a generation system that addresses the cold-start zero-shot clarifying question challenge in conversational search, which is another example of interactive systems that engage users in a step-by-step dialogue to clarify their intent. Furthermore, the research by Qian et al. (2022) focuses on resolving ambiguities in text-to-image generative models through a disambiguation framework that engages users in a step-by-step dialogue to clarify their intent. For instance, the system proposed by Li et al. (2020) employs a parser-independent interactive approach, allowing users to refine their queries based on feedback and disambiguate potential misunderstandings. This interactive process enhances query understanding and improves the accuracy of the generated SQL queries.\\n\\nAnother technique for addressing ambiguity is the use of disambiguation techniques within the model itself. For instance, word sense disambiguation is one of the areas in NLP that has gained significant attention and numerous works have been proposed in this regards ( Wang and Wang ,2021 ). Resolving ambiguities in question answering ( Min et al. ,2020 ), conversational question answering ( Guo et al. ,2021 ), and task-oriented dialogue systems ( Qian et al. ,2022 ) has also been previously studied. Ambiguity resolution has also been studied in multi-modal applications, such as multi-modal machine translation ( Li et al. ,2022 )or matching images or videos to disambiguated interpretation of a sentence ( Berzak et al. ,2015 ). Despite those recent efforts, not much attention has been paid to ambiguities in text-to-image generative models. On the other hand, the growing popularity of those models, both in academic and non-academic circles, make it imperatives to better understand potential issues with those systems due to language ambiguity. In this paper we have identified and addressed some of those issues. We hope that our work will inspire future effort on this important problem. For example, the AmbiQT benchmark (Wang et al., 2022) introduces a dataset with ambiguous queries, each having two distinct valid SQL interpretations. The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs. Inspired by our experience with several real-world datasets, we target four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity. This benchmark encourages the development of Text2SQL models capable of handling ambiguity by considering multiple interpretations and ranking them based on their relevance to the query.\\n\\nFurthermore, the work by Pourreza and Rafiei (2023) highlights the importance of cautious interpretation of benchmark evaluations. They demonstrate that achieving perfect performance on existing benchmarks is unfeasible due to the inherent ambiguity in natural language queries. Their evaluation reveals that the true performance of Text2SQL models may be underestimated, emphasizing the need for additional independent evaluations and the consideration of multiple valid interpretations in benchmark design.\\n\\nIn conclusion, addressing ambiguity in Text2SQL remains an active area of research. Ambiguity in SQL has been studied in other fields of NLP, but it has been unexplored in the context of semantic parsing. AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion. Interactive systems, disambiguation techniques, and careful interpretation of benchmark evaluations are essential for developing accurate and robust Text2SQL models capable of handling the complexities of natural language queries.',\n",
       " '\\n## 2.6 Ethical Considerations\\n\\nThe ethical implications of Text2SQL technology cannot be overlooked, especially considering its potential applications in sensitive domains like healthcare, finance, and government. While Text2SQL systems offer immense benefits by democratizing access to data, they also raise concerns regarding data privacy, fairness, and potential biases inherited from training data. For instance, systems trained on large-scale unfiltered data can suffer from degenerated and biased behavior, which can reflect and reinforce societal biases and structural inequalities. Additionally, the risks associated with neural rendering studies, such as privacy and security issues linked to the capture of sensitive information, are also relevant to Text2SQL systems. Furthermore, the potential negative impact on society from generating editable 3D shapes without 3D supervision, such as the generation of deep fakes, is a concern that must be addressed.\\n\\nRecent studies, such as the work by Liu et al. (2023), have uncovered social biases in Text2SQL models, highlighting the need for careful consideration of the potential consequences of deploying these systems in real-world applications. Text-to-SQL models bridge the gap between database manipulation and amateur users and are mainly applied by administrative industries, such as banks, schools, and governments, which rely on AI-based applications to manipulate databases and further develop policies that will have profound impacts on various aspects of many people’s lives. If there are unwanted prejudices against specific demographics in applied Text-to-SQL models, these stereotypes can be significantly amplified since their retrieval results are adopted by administrative industries to draft policies. Unfortunately, large pre-trained language models (PLMs) are actually acknowledged to contain social biases towards different demographics, and these wicked biases are observed to be inherited by downstream tasks. Some may suppose that these harmful biases could be forgotten or mitigated when fine-tuned on downstream neutral data that does not contain any toxic words, specific demographic keywords, or any judgmental expressions. However, as we observed through experiments, social biases are integrally inherited by downstream models even fine-tuned on neutral data, as in the Text-to-SQL task.\\n\\nThese biases can manifest in various forms, including stereotypical correlations between judgmental expressions and different demographics, as well as incorrect comparisons that perpetuate harmful stereotypes. For instance, the words associated with unmarked, White GPT-3.5 personas include neutral, everyday descriptions, such as good, while those associated with other groups tend not to (Table 3). Similarly, friendly and casually are top words for man personas. On the other hand, generated personas of marked groups reproduce problematic archetypes. Middle-Eastern personas disproportionately mention religion (faith, religious, headscarf). This conflation of Middle-Eastern identity with religious piety—and specifically the conflation of Arab with Muslim—has been criticized by media scholars for dehumanizing and demonizing Middle-Eastern people as brutal religious fanatics (Muscati, 2002; Shaheen, 2003). Also, the words differentiating several marked race/ethnic groups from the default one (White) include culture, traditional, proud, and heritage. These patterns align with previous findings that those in marked groups are defined primarily by their relationship to their demographic identity, which continues to set these groups apart in contrast to the default of whiteness (Frankenburg, 1993; Pierre, 2004; Lewis, 2004). Similarly, the words for nonbinary personas, such as gender, identity, norms, and expectations, exclusively focus on the portrayed individual’s relationship to their gender identity. The words for Middle-Eastern and Asian personas connect to critiques of Orientalism, a damaging depiction where the East (encompassing Asia and the Middle East) is represented as the “ultimate Other” against which Western culture is defined; inaccurate, romanticized representations of these cultures have historically been used as implicit justification for imperialism in these areas (Said, 1978; Ma, 2000; Yoshihara, 2002). By pigeonholing particular demographic groups into specific narratives, the patterns in these generations homogenize these groups rather than characterizing the diversity within them. This reflects essentialism: individuals in these groups are defined solely by a limited, seemingly-fixed essential set of characteristics rather than their full humanity (Rosenblum and Travis, 1996; Woodward, 1997). Essentializing portrayals foster the othering of marked groups, further entrenching their difference from the default groups of society (Brekhus, 1998; Jensen, 2011; Dervin, 2012).\\n\\nTo address these concerns, researchers have proposed several approaches. The BiaSpider benchmark (Liu et al., 2023) aims to uncover and categorize social biases in Text2SQL models by introducing a new paradigm for structured data bias measurement  . This benchmark provides a valuable tool for evaluating and mitigating biases in Text2SQL systems.\\n\\nAdditionally, the work by Awasthi et al. (2023) emphasizes the importance of reviewing Text2SQL systems for harmful biases before deployment and ensuring that users are aware of the potential for incorrect answers . This highlights the need for responsible development and deployment of Text2SQL technology, with a focus on fairness, transparency, and accountability.\\n\\nIn conclusion, while Text2SQL technology offers significant benefits, it is crucial to address the ethical considerations associated with its use. The integration of ChatGPT in studies carries ethical implications with broad social ramifications. It enables inclusive communication but raises concerns about misinformation and biases. Ethical considerations demand transparency, bias mitigation, and ongoing evaluation to harness its benefits responsibly. Our goal with REFILL is to synthesize parallel data for adapting Text-to-SQL parsers to new schemas. We believe that the real-world deployment of Text-to-SQL or any semantic parser trained on text generated by language models must go through a careful review of any harmful biases. Also, the intended users of any Text-to-SQL service must be made aware that the answers generated by these systems are likely to be incorrect.',\n",
       " '\\n## 3 Current Benchmarks and Models',\n",
       " \"\\n## 3.1 Benchmarks\\n\\nThe evaluation of Text2SQL models heavily relies on the availability of comprehensive and diverse benchmarks, such as WikiSQL ( Zhong et al. ,2018 ), SPIDER ( Yu et al. ,2018 ), KaggleDBQA ( Lee et al. ,2021 ), SEDE ( Hazoom et al. ,2021 ), and EHRSQL ( Lee et al. ,2022 ). These benchmarks help in assessing the performance and robustness of models, as well as addressing the problem of ambiguity in real-world datasets. Dr. SPIDER ( Chang et al. ,2023 ) is another benchmark designed to test the robustness of existing models by perturbing either the text or schema of SPIDER. Several prominent benchmarks have emerged in the Text2SQL domain, each with its unique characteristics and challenges. For instance, WikiSQL ( Zhong et al. ,2018 )and SPIDER ( Yu et al. ,2018 ) are popular benchmarks that focus on basic tasks, while benchmarks like KaggleDBQA ( Lee et al. ,2021 ), SEDE ( Hazoom et al. ,2021 ), and EHRSQL ( Lee et al. ,2022 ) aim to capture real-world scenarios. Dr. SPIDER ( Chang et al. ,2023 ) tests the robustness of existing models by perturbing either the text or schema. The AmbiQT benchmark ( Wang et al. ,2022 ) represents the first open benchmark for testing coverage of ambiguous alternatives in Text-to-SQL conversion. Additionally, Text2Analysis ( He et al. ,2023 ) addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis. These benchmarks collectively present a considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities. \\n\\nOne of the most widely used benchmarks is WikiSQL (Zhong et al., 2017), which consists of natural language questions paired with corresponding SQL queries on a single database. WikiSQL focuses on simple and straightforward queries, making it suitable for evaluating the basic capabilities of Text2SQL models. However, its limited scope and lack of complex queries may not adequately reflect the challenges encountered in real-world scenarios. For example, the KITAB dataset, which focuses on literature queries, demonstrates that even state-of-the-art LLMs like GPT4 and GPT3.5 struggle with constraint satisfaction and often produce irrelevant information, with full correctness remaining notably lower than 35% . Furthermore, the evaluation of LLMs on complex queries with several constraint types and longer outputs is still a major challenge, as many existing benchmarks have saturated and do not provide a comprehensive assessment of LLM performance in these scenarios .\\n\\nTo address this limitation, the Spider benchmark (Yu et al., 2018) was introduced. Spider encompasses a diverse set of complex and cross-domain questions, spanning multiple databases with varying schemas. Spider has become a benchmark of choice for evaluating the generalization capabilities of Text2SQL models.\\n\\nAnother notable benchmark is SParC (Yu et al., 2019), which focuses on cross-domain semantic parsing in context. This benchmark addresses the limitations of previous benchmarks by providing a large-scale dataset that covers multiple specific domains for Chinese passage retrieval, including E-commerce, Entertainment video, and Medical. Each domain contains millions of passages and sufficient human-annotated query-passage related pairs, collected from real search engine systems within Alibaba Group. The authenticity of the samples allows SParC to meet the needs of both academia and industry fields, pushing forward the quality and variety of Chinese passage retrieval datasets. SParC provides a more realistic setting by including multi-turn dialogues and context-dependent questions. This benchmark evaluates the ability of Text2SQL models to maintain context and generate accurate queries based on previous interactions.\\n\\nAmbiQT (Wang et al., 2022) is a recent benchmark that specifically targets the issue of ambiguity in Text2SQL. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, and encompasses four types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates. The benchmark is generated via a combination of ChatGPT (OpenAI, 2022) based synonym generation and perturbation, and standard rule-based perturbation. It consists of natural language questions with multiple valid SQL interpretations, each representing a different interpretation of the user\\\\'s intent. AmbiQT challenges Text2SQL models to handle ambiguity and rank multiple interpretations based on their relevance to the query. AmbiQT includes over 3000 examples, each associating a natural question on a database with two valid SQLs, targeting four types of ambiguity spanning both lexical (ambiguous column and table names) and structural (whether a join is necessary, and an aggregate is pre-computed) ambiguity. When faced with ambiguity, an ideal Text-toSQL system should incorporate all valid alternatives in their top$k$ SQL outputs, for user resolution. We show that present approaches, ranging from T5-3B to SOTA models, fail to generate all ambiguous outputs with any decoding strategy, including beam search and diversity-promoting sampling methods such as Nucleus and Typical sampling. Most outputs are small lexical tweaks of the top choice, bringing about little meaningful diversity in SQL structures or schema alternatives. Even SOTA LLMs like ChatGPT suffer from this issue.\\n\\nWikiSQL, Spider, SParC, and AmbiQT each contribute to assessing different aspects of Text2SQL models, from basic query generation to handling ambiguity and context-dependent questions. WikiSQL ( Zhong et al. ,2018 ) and SPIDER ( Yu et al. ,2018 ) are popular benchmarks for the Textto-SQL task, while AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives. AmbiQT is constructed so that each text query has two distinct valid SQL interpretations, encompassing four types of ambiguity: Column Ambiguity (C), Table Ambiguity (T), Join Ambiguity (J), and Precomputed Aggregates (P). This benchmark tests performance under ambiguity in the context of current models, addressing the lack of evaluation of Text-to-SQL models under ambiguity in contemporary literature.\\n\\nThese benchmarks continue to play a crucial role in driving advancements in Text2SQL technology and ensuring the development of accurate and robust natural language interfaces to databases.\\n\\n### 3.2 Models\\n\\nThe landscape of Text2SQL models has evolved significantly, with various architectures and approaches being explored to tackle the complexities of natural language understanding and database schema reasoning. This subsection delves into the key models that have shaped the current state of Text2SQL technology.\\n\\n**Sequence-to-Sequence Models:**\\n\\nOne of the earliest and most influential approaches to Text2SQL is the sequence-to-sequence model. This architecture, inspired by neural machine translation, employs an encoder-decoder framework to translate natural language questions into SQL queries. The encoder processes the input question and encodes it into a fixed-length vector, while the decoder decodes this vector into the target SQL query. Notable examples of sequence-to-sequence models in Text2SQL include Seq2SQL (Zhong et al., 2017) and RATSQL (Wang et al., 2020a). These models demonstrated the effectiveness of neural networks in capturing the intricacies of natural language and database schemas, laying the foundation for subsequent advancements.\\n\\n**Graph-Based Models:**\\n\\nGraph-based models have gained traction in Text2SQL due to their ability to represent the structured nature of database schemas. These models utilize graph structures to encode the relationships between tables, columns, and cell values, enabling more effective reasoning and query generation. Examples of graph-based models include GraphSQL (Yao et al., 2019) and Graphix-T5 (Li et al., 2023b), which leverage graph neural networks to capture the dependencies and relationships within the database schema. These models have shown promising results in handling complex queries and improving the accuracy of generated SQL queries.\\n\\n**Hybrid Models:**\\n\\nHybrid models combine elements from both sequence-to-sequence and graph-based models to leverage their respective strengths. These models often employ a sequence-to-sequence architecture for the overall query generation process while incorporating graph-based components to handle schema reasoning and complex relationships. An example of a hybrid model is RESDSQL (Li et al., 2023a), which decouples schema linking and skeleton parsing to improve the accuracy and efficiency of Text2SQL systems.\\n\\n**Large Language Models (LLMs):**\\n\\nThe integration of LLMs into Text2SQL has revolutionized the field, offering unprecedented levels of language understanding and context-awareness. Fine-tuning pre-trained LLMs like BERT (Devlin et al., 2019) and GPT (Radford et al., 2018) on Text2SQL tasks has led to significant improvements in query generation accuracy. Models like Grappa (Yu et al., 2020) and T5 (Raffel et al., 2020) demonstrate the potential of LLMs in capturing the nuances of natural language and generating accurate SQL queries.\\n\\n**Interactive Systems:**\\n\\nInteractive Text2SQL systems play a crucial role in addressing the ambiguity inherent in natural language queries. These systems engage users in a step-by-step dialogue to clarify their intent and disambiguate potential misunderstandings. Examples of interactive systems include the one proposed by Li et al. (2020), which employs a parser-independent interactive approach to enhance query understanding and improve the accuracy of generated SQL queries.\\n\\nIn conclusion, the current landscape of Text2SQL models is diverse and evolving, with various architectures and approaches being explored to tackle the challenges of natural language understanding and database schema reasoning. Sequence-to-sequence models, graph-based models, hybrid models, LLMs, and interactive systems each contribute to the advancement of Text2SQL technology, paving the way for more accurate and user-friendly natural language interfaces to databases.\",\n",
       " '\\n## 4 Limitations and Future Directions',\n",
       " \"\\n## 4.1 Evaluation Metrics\\n\\nThe evaluation of Text2SQL models is crucial for assessing their performance and driving further research and development. This is evident in the Text2Analysis benchmark, which addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis, providing a comprehensive taxonomy of advanced analysis and unclear queries, which enables the evaluation of the analytical abilities of large language models. Additionally, the evaluation of five state-of-the-art models on the Text2Analysis dataset reveals their strengths and weaknesses in handling advanced analysis tasks and unclear queries, providing valuable insights for future research. However, the current evaluation metrics have limitations that need to be addressed to ensure a comprehensive and accurate assessment of model capabilities. For instance, comparisons are limited to publicly available checkpoints, which can lead to significant confounding variables due to differences in training recipes and datasets. Additionally, the focus on specific aspects of 3D awareness, such as single-image surface reconstruction and multiview consistency, may not provide a comprehensive understanding of a model's 3D capabilities. Furthermore, the reliance on probing methods like linear probes and zero-shot analysis may not fully capture the model's ability to adapt to 3D tasks.\\n\\n**Exact Set Match Accuracy:** This metric measures the percentage of model-generated SQL queries that exactly match the reference SQL queries in the benchmark. While it provides a straightforward measure of accuracy, it fails to capture the nuances of SQL queries that may be semantically equivalent but differ in syntactic structure. For example, the evaluation of table exact match accuracy treats partial matches as incorrect, which may not be ideal for queries that do not impose ordering among columns or rows. Additionally, the reliance on lexical match to measure model effectiveness may not fully capture the underlying meaning of paraphrased sequences, as tables store factual information in an ordered manner. This limitation can lead to underestimating the true performance of Text2SQL models, as demonstrated by Pourreza and Rafiei (2023).\\n\\n**Execution Accuracy:** Execution accuracy evaluates the percentage of model-generated SQL queries that produce the same output as the reference queries when executed against the database. While this metric addresses the limitations of exact set match accuracy by considering the query results, it still has its drawbacks. It assumes that the reference queries are error-free and may not account for alternative valid queries that could also produce correct results. Additionally, execution accuracy can be affected by ties in the database, where multiple rows satisfy the query conditions, leading to potential discrepancies in the evaluation results. For example, when a query asks for the top rows that satisfy certain conditions, such as the student with the highest GPA or the youngest student, and there is a tie for the top position, the corresponding SQL query may return all ties or only one. This becomes a problem in evaluation if a model-generated query and the reference query treat the ties differently. Furthermore, the use of the LIMIT n clause in SQL queries can also lead to ties, particularly when there is a tie on row n with multiple rows having the same values. The ordering among tied rows can vary between two queries, and so is the first n rows that are returned. Another issue arises from the incorrect usage of non-aggregated columns in both the SELECT clause and the GROUP BY clause, which can result in multiple records being associated with the same grouping column or aggregation value, whereas each group can only return one record. These ties and ambiguities can lead to discrepancies in the evaluation results and affect the execution accuracy of text-to-SQL models.\\n\\n**Limitations and Potential Improvements:** To address the limitations of current evaluation metrics, several potential improvements can be considered. First, incorporating semantic equivalence checks that go beyond syntactic matching can provide a more accurate assessment of query correctness. Semantic entropy improves over baselines in predicting whether a model’s answer to a question is correct. This can be achieved by leveraging techniques like query rewriting and normalization to identify semantically equivalent queries. Query rewriting aims to train a rewriting model to mimic human-rewritten queries, which can solve ambiguous problems and recover missing elements from the context. Query expansion methods, such as selecting terms via the normalization score of their embeddings, can also enhance search queries and produce better retrieval results. Integrating both query rewriting and query expansion can reformulate better conversational queries. Second, incorporating multiple reference queries for each natural language question can account for the inherent ambiguity in natural language and provide a more comprehensive evaluation of model performance. For instance, the AmbigQA dataset measures a model’s ability to disambiguate-and-answer ambiguous questions, such as determining the specific game in the 'Fallout' series being referred to in a query like “Where does the new fallout game take place?” and then providing the correct location, “Appalachia”. Furthermore, SituatedQA focuses on temporal and geographic ambiguity, where additional time ranges and their corresponding answers are crowdsourced, and geographic questions are created by removing references to location and then crowdsourcing locations and corresponding answers. These datasets demonstrate the importance of accounting for ambiguity in natural language questions to improve model performance and calibration. Third, developing evaluation metrics that consider the diversity of generated queries and their relevance to the user's intent can provide a more nuanced understanding of model capabilities. For example, in the context of multimodal fusion, it has been observed that increased data diversity can lead to substantial improvements in performance, especially in scarce data regimes. Furthermore, fine-grained evaluation tests can be designed to assess specific model capabilities, such as understanding of ontology, logical equivalence, and answering under visual obfuscation.\\n\\nIn conclusion, while current evaluation metrics have played a crucial role in assessing Text2SQL models, their limitations need to be addressed to ensure a more accurate and comprehensive evaluation. For instance, denotation accuracy, widely used in semantic parsing, is not directly applicable to tasks where tabular input encoding, reasoning, and generation are performed by the same model. Additionally, the strict binary measure of table exact match may not be ideal for queries that do not impose ordering among columns or rows. Furthermore, the limitations in training and benchmarking, as well as the need for more diverse and larger human evaluation, highlight the importance of exploring more evaluation approaches and metrics. By incorporating semantic equivalence checks, considering multiple reference queries, and evaluating query diversity and relevance, we can drive further advancements in Text2SQL technology and develop more robust and accurate natural language interfaces to databases.\\n\\nTo address the limitations of current evaluation metrics, several potential improvements can be considered. For instance, in the context of spatio-temporal prediction across diverse disciplines, limitations such as training limitations, benchmark limitations, and evaluation limitations have been identified. Training limitations include the constraint on model architecture and size, which may be improved by exploring specific architecture enhancements or larger models. Benchmark limitations involve the scope of methods included and the calibration of dataset protocols, suggesting a need for a wider method spectrum and further work on aspects like the impact of the number of input frames. Evaluation limitations highlight the need for a more diverse and larger pool of participants in human evaluations, as well as the exploration of additional evaluation approaches and metrics for a more holistic assessment of models. These insights are drawn from studies that have examined the prevalent methods, representative datasets, and powerful benchmarks in the field, acknowledging that while progress has been made, there is still much to be done to refine evaluation metrics.\",\n",
       " '\\n## 4.2 Combining Text2SQL with Other Tasks\\n\\nCombining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases. By integrating Text2SQL with tasks like question answering, information extraction, and natural language generation, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences.\\n\\nOne potential area of integration is with question answering (QA) systems. By combining Text2SQL with QA, we can build systems that can not only translate natural language questions into SQL queries but also answer those questions directly using the retrieved data. This integration can be achieved by incorporating QA models into the Text2SQL pipeline, allowing the system to generate natural language answers based on the results of the executed SQL queries. This approach can provide a more user-friendly and intuitive interface for interacting with databases, as users can pose questions in natural language and receive answers in a similar format.\\n\\nAnother area of integration is with information extraction (IE) tasks. By combining Text2SQL with IE, we can build systems that can extract structured information from unstructured text sources and store it in databases. This integration can be achieved by incorporating IE models into the Text2SQL pipeline, allowing the system to extract relevant information from text sources and generate SQL queries to insert or update the extracted data in the database. For instance, UniEX: an Effective and Efficient Framework for Unified Information Extraction Via a Span-extractive Perspective () demonstrates the potential of using a unified extractive framework for various IE tasks, which can be beneficial for the Text2SQL pipeline. Additionally, the work on Benchmarking and Improving Text-to-SQL Generation under Ambiguity () highlights the importance of addressing ambiguity in SQL generation, a critical aspect when integrating IE models into the Text2SQL process. This approach can facilitate the automated creation and maintenance of databases, as well as enable more sophisticated data analysis and retrieval tasks.\\n\\nFurthermore, integrating Text2SQL with natural language generation (NLG) tasks can enable the generation of natural language explanations and summaries of query results. This can enhance the interpretability and accessibility of query results, making it easier for users to understand and analyze the retrieved data. For example, the system proposed by Kokkalis et al. (2012) translates SQL queries into narratives, providing users with a more intuitive understanding of the query results.\\n\\nIn conclusion, combining Text2SQL with other NLP tasks offers a promising direction for advancing natural language interfaces to databases. By integrating Text2SQL with QA, IE, and NLG tasks, we can create more powerful and versatile systems capable of handling complex user queries and providing rich information retrieval experiences. This integration can lead to the development of more user-friendly, efficient, and intelligent natural language interfaces to databases, empowering users to access and analyze data more effectively.',\n",
       " \"\\n## 4.3 Addressing Bias\\n\\nAddressing bias in Text2SQL systems is of paramount importance, especially considering their potential applications in sensitive domains like healthcare, finance, and government.  Biased Text2SQL models can perpetuate and amplify existing stereotypes, leading to unfair and discriminatory outcomes.  Therefore, it is crucial to develop methods for identifying, mitigating, and eliminating bias in these systems.\\n\\nOne approach to addressing bias is through the use of diverse and representative training data.  By ensuring that the training data encompasses a wide range of perspectives and demographics, we can reduce the likelihood of biased model predictions. Techniques like data augmentation and synthetic data generation can be employed to create more diverse training datasets and improve the generalizability of Text2SQL models. \\n\\nAnother important strategy is to incorporate bias mitigation techniques during the model development process.  This can involve using techniques like adversarial training, which aims to minimize the model's reliance on biased features, or incorporating fairness constraints into the training objective. These techniques can help ensure that the model treats different demographic groups fairly and avoids perpetuating harmful stereotypes. \\n\\nFurthermore, it is crucial to evaluate Text2SQL models for bias and fairness before deployment.  This can be achieved through the use of bias detection tools and fairness metrics, which can help identify potential biases in the model's predictions. By carefully evaluating and addressing bias, we can ensure that Text2SQL systems are fair, transparent, and accountable. \\n\\nIn conclusion, addressing bias in Text2SQL systems is an essential step towards building fair and responsible natural language interfaces to databases.  By incorporating diverse training data, bias mitigation techniques, and rigorous evaluation procedures, we can develop Text2SQL models that are not only accurate and efficient but also ethical and trustworthy.\",\n",
       " \"\\n## 4.4 Future Research Directions\\n\\nThe field of Text2SQL is rapidly evolving, with numerous opportunities for future research and development. This subsection explores several promising directions that can further advance Text2SQL technology and broaden its applications.\\n\\n**Advanced NLP Techniques:**\\n\\nIntegrating more advanced NLP techniques into Text2SQL models can significantly enhance their understanding of natural language and improve query generation accuracy. For instance, the use of chain-of-thought prompting has been shown to improve performance on text-to-SQL parsing tasks, as demonstrated by the question-decomposition prompting method (QDecomp) which outperforms existing prompting methods by 2.4 and 1.5 point absolute gains on the development set of Spider and Spider Realistic datasets. For instance, incorporating techniques like dependency parsing, coreference resolution, and semantic role labeling can help models better capture the relationships between different entities in the query and generate more accurate SQL queries. Exploring the use of transformer-based models with attention mechanisms can also enable models to better handle long-range dependencies and complex sentence structures.\\n\\n**Combining Text2SQL with Other Tasks:**\\n\\nCombining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems. For example, integrating Text2SQL with question answering systems can enable users to pose questions in natural language and receive answers directly without the need for intermediate SQL queries. Combining Text2SQL with information extraction tasks can facilitate the automated creation and maintenance of databases by extracting structured information from unstructured text sources. Integrating Text2SQL with natural language generation tasks can enable the generation of natural language explanations and summaries of query results, enhancing interpretability and accessibility.\\n\\n**Addressing Real-world Challenges:**\\n\\nDeveloping Text2SQL systems that can handle real-world challenges like ambiguity, noise, and domain-specific language is crucial for practical applications. Ambiguity in SQL, arising from related column names, has been studied in (Wang et al., 2022), but they only consider column ambiguity. Their method of recognizing ambiguous queries depends on labeling words of the text and does not generalize to other kinds of ambiguity. To the best of our discernment, AmbiQT represents the first open benchmark for testing coverage of ambiguous alternatives. AmbiQT is constructed so that each text query has two distinct valid SQL interpretations. Motivated by our experience working with real-life databases, we designed AmbiQT to encompass four types of ambiguity: Column Ambiguity (C), Table Ambiguity (T), Join Ambiguity (J), and Precomputed Aggregates (P). Each entry is designed so that both alternatives have a similar relevance to the question, and a well-calibrated decoding method is expected to rank them close by in their outputs. Exploring techniques like interactive systems, disambiguation methods, and domain adaptation can help models better handle these challenges and improve their performance in real-world scenarios. For instance, domain adaptation techniques have been shown to improve generalization performance by simulating domain shift through a training procedure that divides the source domain into meta-train and meta-test domains. Additionally, disentangled representation learning can disentangle features into a domain-invariant content space and a domain-specific attribute space, thus learning a domain-invariant representation from data across multiple domains. Furthermore, recent studies demonstrate that pre-trained models can bring out-of-distribution generalization capabilities. Additionally, investigating the use of transfer learning and few-shot learning can enable models to quickly adapt to new domains and tasks with limited training data. For example, the best model performs less than $30\\\\\\\\%$ accuracy for the 5- shot setting on the most difficult ChestX dataset [56]. This reveals that common knowledge like ImageNet [52] can only provide a reasonable distribution for initialization, but it is very hard to learn the real expert knowledge in some medical applications. Moreover, the setting we explored is still under the $N$ -way $K$ -shot learning system, while the real-world demands often require an adaptive $X$ -way or $Y$ -shot for both learning and inference, which should also be explored in future work. We believe that this could be solved by learning adaptive reprojections and alignment strategies which are highly related to input instances.\\n\\n**Ethical Considerations and Bias Mitigation:**\\n\\nContinuing to address ethical considerations and bias mitigation in Text2SQL systems is essential for building fair and responsible natural language interfaces to databases. This involves incorporating diverse and representative training data, bias mitigation techniques, and rigorous evaluation procedures to ensure that Text2SQL models are not only accurate and efficient but also ethical and trustworthy. This involves incorporating diverse and representative training data, bias mitigation techniques, and rigorous evaluation procedures to ensure that Text2SQL models are not only accurate and efficient but also ethical and trustworthy.\\n\\nIn conclusion, the future of Text2SQL is bright, with numerous opportunities for research and development. By exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations, we can further advance Text2SQL technology and unlock its full potential for empowering users to access and analyze data more effectively. \\n\\nCombining Text2SQL with other NLP tasks like question answering, information extraction, and natural language generation can create more powerful and versatile systems. For example, the integration of Text2SQL with interactive semantic parsing for SQL allows users to validate and refine generated queries through step-by-step explanations, enhancing the overall system's performance and user experience. Additionally, incorporating Text2SQL with natural language explanations for SQL queries can improve the accessibility and interpretability of the system. Furthermore, the combination of Text2SQL with retrieval enhancement techniques can generate more diverse and accurate text, increasing the system's versatility. Finally, the integration of Text2SQL with human-in-the-loop approaches can facilitate the generation of high-quality data with accurate diversification, further enhancing the system's capabilities.\",\n",
       " \"\\n## 5 Conclusion\\n\\nThe Text2SQL task has seen significant advancements in recent years, driven by the integration of deep learning, large language models, and interactive systems. This research survey has provided a comprehensive overview of the current state of Text2SQL technology, exploring its evolution, key benchmarks and models, limitations, and future directions. The Text2Analysis benchmark is proposed as a new benchmark to further explore LLMs’ upper limits in challenging tabular data analysis tasks . We have presented the Text2Analysis dataset that addresses the research gap in advanced analysis tasks and unclear queries in the context of tabular data analysis . A Text-to-SQL model takes as input a question expressed as a natural language text x, and a database schema comprising of table and column names, and outputs an SQL program y which can be executed against the database to answer the user’s question . In this paper, we propose to uncover and categorize social biases in the Text-to-SQL task .\\n\\nThe survey began by discussing the background and related work, highlighting the early developments in Text2SQL, the impact of deep learning and large language models, and techniques for data augmentation and ambiguity handling. It then delved into the current benchmarks and models, analyzing popular benchmarks like WikiSQL and Spider, and examining different Text2SQL models, including sequence-to-sequence models, graph-based models, and hybrid models. The survey continued by discussing the limitations of current Text2SQL systems and proposing potential solutions and future research directions. This included a critical analysis of evaluation metrics, the potential for combining Text2SQL with other NLP tasks, methods for addressing bias, and new research directions for advancing Text2SQL technology.\\n\\nThe survey concludes by summarizing the key findings and highlighting the potential impact of Text2SQL technology. The integration of neural networks, LLMs, and interactive systems has revolutionized the field, leading to more accurate, robust, and user-friendly Text2SQL systems. However, challenges remain in addressing ambiguity, bias, and real-world complexities. For instance, preferences and values are not universal, and they are often inconsistently defined . Additionally, human feedback is inherently incomplete, and operationalizing a 'good' output is difficult . Furthermore, crowdworkers and social media users are neither representative nor sufficient, which can lead to biased outcomes . Future research directions include exploring advanced NLP techniques, combining Text2SQL with other tasks, addressing real-world challenges, and prioritizing ethical considerations. By continuing to advance Text2SQL technology, we can unlock its full potential for empowering users to access and analyze data more effectively.\"]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([{'statement': 'Addressing the challenges and limitations of editing multimodal large language models (MLLMs) requires further research and development in several key areas.',\n",
       "   'related_sen_id': [0],\n",
       "   'statement_hyde': 'The necessity for additional research and development to tackle the inherent challenges and limitations in the editing of multimodal large language models (MLLMs) has been underscored by various studies.'},\n",
       "  {'statement': \"Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge.\",\n",
       "   'related_sen_id': [1, 2],\n",
       "   'statement_hyde': \"Methods for achieving cross-modal alignment in edits, which are crucial for maintaining consistency and coherence within the model's knowledge base, have been explored in recent literature, including techniques such as multi-task learning and co-attention mechanisms.\"},\n",
       "  {'statement': 'Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors.',\n",
       "   'related_sen_id': [3, 4],\n",
       "   'statement_hyde': 'The development of robust editing techniques capable of managing noisy or incomplete data is essential to mitigate the introduction of biases or errors, as demonstrated by studies employing robust optimization and adversarial training.'},\n",
       "  {'statement': \"Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities.\",\n",
       "   'related_sen_id': [5, 6, 7, 8, 9, 10],\n",
       "   'statement_hyde': 'The establishment of standardized multimodal evaluation metrics, which comprehensively assess the impact of edits on model knowledge and performance, has been proposed. These metrics include aspects such as Portability, Locality, and various accuracy measures, as detailed in recent research.'},\n",
       "  {'statement': 'Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency.',\n",
       "   'related_sen_id': [11, 12],\n",
       "   'statement_hyde': 'The ethical considerations surrounding MLLM editing, along with the development of guidelines for responsible usage to ensure fairness and transparency, have been highlighted in the literature, emphasizing the need for frameworks for auditing and bias mitigation.'},\n",
       "  {'statement': 'By addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications.',\n",
       "   'related_sen_id': [13],\n",
       "   'statement_hyde': 'It has been posited that addressing the identified challenges and limitations will significantly advance the field of MLLM editing, thereby unlocking the full potential of these models across diverse applications.'},\n",
       "  {'statement': 'This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.',\n",
       "   'related_sen_id': [14],\n",
       "   'statement_hyde': 'The resolution of these issues is anticipated to facilitate the development of more accurate, reliable, and fair AI systems, which are capable of effectively processing and comprehending the complexities inherent in real-world data.'}],\n",
       " \"\\n## 5.2 Future Research Directions\\n\\nAddressing the challenges and limitations of editing multimodal large language models (MLLMs) requires further research and development in several key areas. These include:\\n\\n* **Cross-Modal Alignment Techniques:** Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge. This could involve exploring techniques like multi-task learning, co-attention mechanisms, and shared representations that facilitate communication and coordination between different modalities.\\n* **Robust Editing Techniques:** Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors. This could involve exploring techniques like robust optimization, adversarial training, and data augmentation to improve the model's resilience to noise and outliers.\\n* **Multimodal Evaluation Metrics:** Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities. This could involve developing metrics that assess the quality of the edited outputs in each modality, the consistency between modalities, and the model's ability to generalize to new tasks and domains. For instance, the concept of Portability is introduced to gauge the effectiveness of model editing in transferring knowledge to related content, termed robust generalization. This involves evaluating three aspects: Subject Replace, Reversed Relation, and One-hop. Additionally, the evaluation of Locality assesses the side effects of model editing, considering Other Relations, Distract Neighbour, and Other Tasks. Furthermore, metrics such as edit-wise success rate, instance-wise accuracy, and multi-hop accuracy are used to measure the success of edits and the model's ability to recall and use edited knowledge consistently.\\n* **Ethical Considerations:** Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency. This could involve establishing frameworks for auditing and certifying edited models, as well as developing tools and techniques for detecting and mitigating biases and misinformation.\\n\\nBy addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications. This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.\")"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modified_sections = sections[-2]\n",
    "statements_0 = statements[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'statement': 'Addressing the challenges and limitations of editing multimodal large language models (MLLMs) requires further research and development in several key areas.',\n",
       "  'related_sen_id': [0],\n",
       "  'statement_hyde': 'The necessity for additional research and development to tackle the inherent challenges and limitations in the editing of multimodal large language models (MLLMs) has been underscored by various studies.'},\n",
       " {'statement': \"Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge.\",\n",
       "  'related_sen_id': [1, 2],\n",
       "  'statement_hyde': \"Methods for achieving cross-modal alignment in edits, which are crucial for maintaining consistency and coherence within the model's knowledge base, have been explored in recent literature, including techniques such as multi-task learning and co-attention mechanisms.\"},\n",
       " {'statement': 'Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors.',\n",
       "  'related_sen_id': [3, 4],\n",
       "  'statement_hyde': 'The development of robust editing techniques capable of managing noisy or incomplete data is essential to mitigate the introduction of biases or errors, as demonstrated by studies employing robust optimization and adversarial training.'},\n",
       " {'statement': \"Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities.\",\n",
       "  'related_sen_id': [5, 6, 7, 8, 9, 10],\n",
       "  'statement_hyde': 'The establishment of standardized multimodal evaluation metrics, which comprehensively assess the impact of edits on model knowledge and performance, has been proposed. These metrics include aspects such as Portability, Locality, and various accuracy measures, as detailed in recent research.'},\n",
       " {'statement': 'Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency.',\n",
       "  'related_sen_id': [11, 12],\n",
       "  'statement_hyde': 'The ethical considerations surrounding MLLM editing, along with the development of guidelines for responsible usage to ensure fairness and transparency, have been highlighted in the literature, emphasizing the need for frameworks for auditing and bias mitigation.'},\n",
       " {'statement': 'By addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications.',\n",
       "  'related_sen_id': [13],\n",
       "  'statement_hyde': 'It has been posited that addressing the identified challenges and limitations will significantly advance the field of MLLM editing, thereby unlocking the full potential of these models across diverse applications.'},\n",
       " {'statement': 'This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.',\n",
       "  'related_sen_id': [14],\n",
       "  'statement_hyde': 'The resolution of these issues is anticipated to facilitate the development of more accurate, reliable, and fair AI systems, which are capable of effectively processing and comprehending the complexities inherent in real-world data.'}]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statements_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\"sen_id:0\\nsentence_text:.## 5.2 Future Research Directions..Addressing the challenges and limitations of editing multimodal large language models (MLLMs) requires further research and development in several key areas.\\nsen_id:1\\nsentence_text:These include:..* **Cross-Modal Alignment Techniques:** Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge.\\nsen_id:2\\nsentence_text:This could involve exploring techniques like multi-task learning, co-attention mechanisms, and shared representations that facilitate communication and coordination between different modalities.\\nsen_id:3\\nsentence_text:* **Robust Editing Techniques:** Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors.\\nsen_id:4\\nsentence_text:This could involve exploring techniques like robust optimization, adversarial training, and data augmentation to improve the model's resilience to noise and outliers.\\nsen_id:5\\nsentence_text:* **Multimodal Evaluation Metrics:** Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities.\\nsen_id:6\\nsentence_text:This could involve developing metrics that assess the quality of the edited outputs in each modality, the consistency between modalities, and the model's ability to generalize to new tasks and domains.\\nsen_id:7\\nsentence_text:For instance, the concept of Portability is introduced to gauge the effectiveness of model editing in transferring knowledge to related content, termed robust generalization.\\nsen_id:8\\nsentence_text:This involves evaluating three aspects: Subject Replace, Reversed Relation, and One-hop.\\nsen_id:9\\nsentence_text:Additionally, the evaluation of Locality assesses the side effects of model editing, considering Other Relations, Distract Neighbour, and Other Tasks.\\nsen_id:10\\nsentence_text:Furthermore, metrics such as edit-wise success rate, instance-wise accuracy, and multi-hop accuracy are used to measure the success of edits and the model's ability to recall and use edited knowledge consistently.\\nsen_id:11\\nsentence_text:* **Ethical Considerations:** Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency.\\nsen_id:12\\nsentence_text:This could involve establishing frameworks for auditing and certifying edited models, as well as developing tools and techniques for detecting and mitigating biases and misinformation.\\nsen_id:13\\nsentence_text:By addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications.\\nsen_id:14\\nsentence_text:This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.\\n\",\n",
       " ['\\n## 5.2 Future Research Directions\\n\\nAddressing the challenges and limitations of editing multimodal large language models (MLLMs) requires further research and development in several key areas.',\n",
       "  \"These include:\\n\\n* **Cross-Modal Alignment Techniques:** Developing methods for aligning edits across different modalities, ensuring consistency and coherence in the model's knowledge.\",\n",
       "  'This could involve exploring techniques like multi-task learning, co-attention mechanisms, and shared representations that facilitate communication and coordination between different modalities.',\n",
       "  '* **Robust Editing Techniques:** Designing editing techniques that can handle noisy or incomplete editing data, mitigating the risk of introducing biases or errors.',\n",
       "  \"This could involve exploring techniques like robust optimization, adversarial training, and data augmentation to improve the model's resilience to noise and outliers.\",\n",
       "  \"* **Multimodal Evaluation Metrics:** Creating standardized evaluation metrics that capture the holistic impact of edits on the model's knowledge and performance across different modalities.\",\n",
       "  \"This could involve developing metrics that assess the quality of the edited outputs in each modality, the consistency between modalities, and the model's ability to generalize to new tasks and domains.\",\n",
       "  'For instance, the concept of Portability is introduced to gauge the effectiveness of model editing in transferring knowledge to related content, termed robust generalization.',\n",
       "  'This involves evaluating three aspects: Subject Replace, Reversed Relation, and One-hop.',\n",
       "  'Additionally, the evaluation of Locality assesses the side effects of model editing, considering Other Relations, Distract Neighbour, and Other Tasks.',\n",
       "  \"Furthermore, metrics such as edit-wise success rate, instance-wise accuracy, and multi-hop accuracy are used to measure the success of edits and the model's ability to recall and use edited knowledge consistently.\",\n",
       "  '* **Ethical Considerations:** Exploring the ethical implications of MLLM editing and developing guidelines for responsible use to ensure fairness and transparency.',\n",
       "  'This could involve establishing frameworks for auditing and certifying edited models, as well as developing tools and techniques for detecting and mitigating biases and misinformation.',\n",
       "  'By addressing these challenges and limitations, we can advance the field of MLLM editing and unlock the full potential of these powerful models for a wide range of applications.',\n",
       "  'This will enable us to build more accurate, reliable, and fair AI systems that can effectively process and understand the complexities of the real world.'])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentence_tokenize(modified_sections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.<sup>asd</sup>\n",
      "However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.<sup>asd</sup>\n",
      "This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.<sup>asd</sup>\n",
      "This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.<sup>asd</sup>\n",
      "To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.<sup>asd</sup>\n",
      "Benchmarks like PredBench have limitations in terms of training, benchmarking, and evaluation.<sup>asd</sup>\n",
      "For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.<sup>asd</sup>\n",
      "Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.<sup>asd</sup>\n",
      "To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.<sup>asd</sup>\n",
      "Additionally, incorporating indicators of attack failures could help in debugging faulty evaluations and lead to fairer assessments.<sup>asd</sup>\n",
      "Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments to evaluate models' ability to exhibit rational behavior in economic tasks.<sup>asd</sup>\n",
      "We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems.<sup>asd</sup>\n",
      "For example, the AmbiQT benchmark addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.<sup>asd</sup>\n",
      "Furthermore, the work on interactive Text-to-SQL generation proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.<sup>asd</sup>\n",
      "Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.<sup>asd</sup>\n",
      "Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.<sup>asd</sup>\n",
      "Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.<sup>asd</sup>\n"
     ]
    }
   ],
   "source": [
    "# 为每个statement创建句子ID到新句子的映射\n",
    "sentence_replacements = []\n",
    "sentences = parsed_sections[\"sentences\"][0]\n",
    "for statement in statements:\n",
    "    new_statement = statement['statement']+\"<sup>asd</sup>\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "new_sentences = []\n",
    "for sen_idx, sentence in enumerate(sentences):\n",
    "        # 如果这个句子ID在替换映射中，使用新的句子\n",
    "        if sen_idx in sentence_replacements:\n",
    "            new_sentences.append(sentence_replacements[sen_idx])\n",
    "        else:\n",
    "            new_sentences.append(sentence)\n",
    "''.join(new_sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.<sup>0</sup>However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.<sup>1</sup>For example, a query asking for the capacity of O2 Arena could be ambiguous if the schema has separate columns for standing and seating capacity.Similarly, a query on the number of under-nourished children is ambiguous if there are different columns for 'under-weight children' and 'stunted growth in children'.This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.<sup>4</sup>This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.<sup>5</sup>To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.<sup>6</sup>This benchmark aims to test the performance of Text-to-SQL systems under ambiguity and encourage the development of more robust and accurate models.In this survey, we explore the current state of Text-to-SQL technology, focusing on the challenges posed by ambiguity and the approaches used to address it.We discuss the limitations of existing benchmarks and evaluation metrics, and propose potential improvements to ensure a more comprehensive and accurate assessment of model capabilities.Benchmarks like PredBench have limitations in terms of training, benchmarking, and evaluation.<sup>10</sup>For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.<sup>11</sup>Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.<sup>12</sup>To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.<sup>13</sup>Additionally, incorporating indicators of attack failures could help in debugging faulty evaluations and lead to fairer assessments.<sup>14</sup>Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments to evaluate models' ability to exhibit rational behavior in economic tasks.<sup>15</sup>We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems.<sup>16</sup>For example, the AmbiQT benchmark addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.<sup>17</sup>Furthermore, the work on interactive Text-to-SQL generation proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.<sup>18</sup>Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.<sup>19</sup>Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.<sup>20</sup>Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.<sup>21</sup>\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 遍历每个section并替换相应的句子\n",
    "for section_idx, section in enumerate(modified_sections):\n",
    "    sentences = parsed_sections['sentences'][section_idx]\n",
    "    new_sentences = []\n",
    "    \n",
    "    for sen_idx, sentence in enumerate(sentences):\n",
    "        # 如果这个句子ID在替换映射中，使用新的句子\n",
    "        if sen_idx in sentence_replacements:\n",
    "            new_sentences.append(sentence_replacements[sen_idx])\n",
    "        else:\n",
    "            new_sentences.append(sentence)\n",
    "            \n",
    "    # 将新的句子组合成一个完整的section\n",
    "    modified_sections[section_idx] = ' '.join(new_sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[1]\n",
      "However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[4]\n",
      "This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[5]\n",
      "This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[6]\n",
      "To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[10]\n",
      "Benchmarks like PredBench have limitations in terms of training, benchmarking, and evaluation.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[11]\n",
      "For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[12]\n",
      "Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[13]\n",
      "To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[14]\n",
      "Additionally, incorporating indicators of attack failures could help in debugging faulty evaluations and lead to fairer assessments.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[15]\n",
      "Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments to evaluate models' ability to exhibit rational behavior in economic tasks.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[16]\n",
      "We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[17]\n",
      "For example, the AmbiQT benchmark addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[18]\n",
      "Furthermore, the work on interactive Text-to-SQL generation proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[19]\n",
      "Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[20]\n",
      "Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n",
      "[21]\n",
      "Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.<sup>1</sup>\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for statement in statements:\n",
    "    print(statement[\"related_sen_id\"])\n",
    "    a = statement[\"statement\"]+\"<sup>1</sup>\"\n",
    "    print(a)\n",
    "    \n",
    "    print(\"-\"*100)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\"sen_id:0\\nsentence_text:.## 1 Introduction..Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.\\nsen_id:1\\nsentence_text:However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.\\nsen_id:2\\nsentence_text:For example, a query asking for the capacity of O2 Arena could be ambiguous if the schema has separate columns for standing and seating capacity.\\nsen_id:3\\nsentence_text:Similarly, a query on the number of under-nourished children is ambiguous if there are different columns for 'under-weight children' and 'stunted growth in children'.\\nsen_id:4\\nsentence_text:This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.\\nsen_id:5\\nsentence_text:This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.\\nsen_id:6\\nsentence_text:To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.\\nsen_id:7\\nsentence_text:This benchmark aims to test the performance of Text-to-SQL systems under ambiguity and encourage the development of more robust and accurate models.\\nsen_id:8\\nsentence_text:In this survey, we explore the current state of Text-to-SQL technology, focusing on the challenges posed by ambiguity and the approaches used to address it.\\nsen_id:9\\nsentence_text:We discuss the limitations of existing benchmarks and evaluation metrics, and propose potential improvements to ensure a more comprehensive and accurate assessment of model capabilities.\\nsen_id:10\\nsentence_text:Benchmarks like PredBench [PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines, 66908e2301d2a3fbfcea14d7, 8] have limitations in terms of training, benchmarking, and evaluation.\\nsen_id:11\\nsentence_text:For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.\\nsen_id:12\\nsentence_text:Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.\\nsen_id:13\\nsentence_text:To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.\\nsen_id:14\\nsentence_text:Additionally, incorporating indicators of attack failures [Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples, 60d140d191e011c16f0cb388, 5] could help in debugging faulty evaluations and lead to fairer assessments.\\nsen_id:15\\nsentence_text:Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments [STEER: Assessing the Economic Rationality of Large Language Models, 65cec1c1939a5f40828f00d7, 11] to evaluate models' ability to exhibit rational behavior in economic tasks.\\nsen_id:16\\nsentence_text:We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems.\\nsen_id:17\\nsentence_text:For example, the AmbiQT benchmark (Benchmarking and Improving Text-to-SQL Generation under Ambiguity, 6535d747939a5f408295c649, 1) addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.\\nsen_id:18\\nsentence_text:Furthermore, the work on interactive Text-to-SQL generation (Interactive Text-to-SQL Generation Via Editable Step-by-Step Explanations, 6461b9c9d68f896efad43133, 1) proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.\\nsen_id:19\\nsentence_text:Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL (Exploring Chain-of-Thought Style Prompting for Text-to-SQL, 646d8642d68f896efa0a3040, 1) aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.\\nsen_id:20\\nsentence_text:Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.\\nsen_id:21\\nsentence_text:Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.\\n\",\n",
       " ['\\n## 1 Introduction\\n\\nResearch on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.',\n",
       "  'However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.',\n",
       "  'For example, a query asking for the capacity of O2 Arena could be ambiguous if the schema has separate columns for standing and seating capacity.',\n",
       "  \"Similarly, a query on the number of under-nourished children is ambiguous if there are different columns for 'under-weight children' and 'stunted growth in children'.\",\n",
       "  'This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.',\n",
       "  \"This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.\",\n",
       "  'To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.',\n",
       "  'This benchmark aims to test the performance of Text-to-SQL systems under ambiguity and encourage the development of more robust and accurate models.',\n",
       "  'In this survey, we explore the current state of Text-to-SQL technology, focusing on the challenges posed by ambiguity and the approaches used to address it.',\n",
       "  'We discuss the limitations of existing benchmarks and evaluation metrics, and propose potential improvements to ensure a more comprehensive and accurate assessment of model capabilities.',\n",
       "  'Benchmarks like PredBench [PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines, 66908e2301d2a3fbfcea14d7, 8] have limitations in terms of training, benchmarking, and evaluation.',\n",
       "  'For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.',\n",
       "  'Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.',\n",
       "  'To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.',\n",
       "  'Additionally, incorporating indicators of attack failures [Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples, 60d140d191e011c16f0cb388, 5] could help in debugging faulty evaluations and lead to fairer assessments.',\n",
       "  \"Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments [STEER: Assessing the Economic Rationality of Large Language Models, 65cec1c1939a5f40828f00d7, 11] to evaluate models' ability to exhibit rational behavior in economic tasks.\",\n",
       "  'We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems.',\n",
       "  'For example, the AmbiQT benchmark (Benchmarking and Improving Text-to-SQL Generation under Ambiguity, 6535d747939a5f408295c649, 1) addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.',\n",
       "  'Furthermore, the work on interactive Text-to-SQL generation (Interactive Text-to-SQL Generation Via Editable Step-by-Step Explanations, 6461b9c9d68f896efad43133, 1) proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.',\n",
       "  \"Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL (Exploring Chain-of-Thought Style Prompting for Text-to-SQL, 646d8642d68f896efa0a3040, 1) aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.\",\n",
       "  'Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.',\n",
       "  'Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.'])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "sentence_tokenize(long_sections[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "statements = [{'statement': 'Research on Text-to-SQL generation has traditionally focused on scenarios where each natural language question is associated with one correct SQL query.',\n",
    "  'related_sen_id': [0],\n",
    "  'statement_hyde': 'Traditional research in the domain of Text-to-SQL generation has predominantly concentrated on scenarios wherein a single natural language query corresponds to a unique, correct SQL query, as documented in [Reference].'},\n",
    " {'statement': 'However, real-world databases often exhibit significant ambiguity in natural language queries due to overlapping schema names, multiple relationship paths, and other factors.',\n",
    "  'related_sen_id': [1],\n",
    "  'statement_hyde': 'Contrary to idealized scenarios, real-world databases frequently present substantial ambiguity in natural language queries, stemming from overlapping schema names, multiple relationship paths, and various other contributing factors, as highlighted in [Reference].'},\n",
    " {'statement': 'This ambiguity can lead to multiple SQL queries that can produce the correct answer, yet most benchmarks only provide one query among the many possible correct answers.',\n",
    "  'related_sen_id': [4],\n",
    "  'statement_hyde': 'Such ambiguity can result in the existence of multiple SQL queries that yield correct answers, although the majority of existing benchmarks typically furnish only a single query from the numerous plausible correct alternatives, as noted in [Reference].'},\n",
    " {'statement': \"This ambiguity poses a challenge for existing Text-to-SQL systems, which struggle to generate accurate and diverse SQL queries that capture all possible interpretations of the user's intent.\",\n",
    "  'related_sen_id': [5],\n",
    "  'statement_hyde': \"The aforementioned ambiguity presents a significant challenge to current Text-to-SQL systems, which often encounter difficulties in generating both accurate and diverse SQL queries that encompass all potential interpretations of the user's intent, as discussed in [Reference].\"},\n",
    " {'statement': 'To address this gap, we develop a novel benchmark called AmbiQT, which consists of over 3000 examples where each natural language query is interpretable as two plausible SQL queries due to lexical and/or structural ambiguity.',\n",
    "  'related_sen_id': [6],\n",
    "  'statement_hyde': 'To bridge this gap, we introduce a novel benchmark, termed AmbiQT, comprising over 3000 examples wherein each natural language query can be interpreted as two viable SQL queries owing to lexical and/or structural ambiguity, as detailed in [Reference].'},\n",
    " {'statement': 'Benchmarks like PredBench have limitations in terms of training, benchmarking, and evaluation.',\n",
    "  'related_sen_id': [10],\n",
    "  'statement_hyde': 'Benchmarks such as PredBench exhibit notable limitations in the realms of training, benchmarking, and evaluation processes, as evidenced in [Reference].'},\n",
    " {'statement': 'For instance, training limitations include the constraint on model architecture and size, while benchmark limitations involve the limited number of methods and the need for further calibration of dataset protocols.',\n",
    "  'related_sen_id': [11],\n",
    "  'statement_hyde': 'For example, training limitations are characterized by constraints on model architecture and size, whereas benchmark limitations are associated with a restricted number of methods and the necessity for additional calibration of dataset protocols, as outlined in [Reference].'},\n",
    " {'statement': 'Evaluation limitations are observed in the small and homogenous sample of human evaluators and the lack of diverse evaluation approaches and metrics.',\n",
    "  'related_sen_id': [12],\n",
    "  'statement_hyde': 'Evaluation limitations are evident in the utilization of a small and homogenous sample of human evaluators, coupled with the absence of diverse evaluation methodologies and metrics, as identified in [Reference].'},\n",
    " {'statement': 'To address these limitations, future studies could explore more evaluation methods, improve the diversity and size of participants, and investigate the impact of various hyperparameters on model performance.',\n",
    "  'related_sen_id': [13],\n",
    "  'statement_hyde': 'To mitigate these limitations, future research endeavors could investigate additional evaluation methods, enhance the diversity and size of participant pools, and examine the influence of diverse hyperparameters on model performance, as suggested in [Reference].'},\n",
    " {'statement': 'Additionally, incorporating indicators of attack failures could help in debugging faulty evaluations and lead to fairer assessments.',\n",
    "  'related_sen_id': [14],\n",
    "  'statement_hyde': 'Furthermore, the integration of indicators of attack failures may facilitate the debugging of erroneous evaluations, thereby contributing to more equitable assessments, as proposed in [Reference].'},\n",
    " {'statement': \"Furthermore, benchmarks could benefit from the inclusion of economic rationality assessments to evaluate models' ability to exhibit rational behavior in economic tasks.\",\n",
    "  'related_sen_id': [15],\n",
    "  'statement_hyde': \"Moreover, the incorporation of economic rationality assessments into benchmarks could prove beneficial for evaluating models' capacity to demonstrate rational behavior in economic tasks, as argued in [Reference].\"},\n",
    " {'statement': 'We also explore the integration of Text-to-SQL with other NLP tasks, such as question answering and information extraction, to create more powerful and versatile systems.',\n",
    "  'related_sen_id': [16],\n",
    "  'statement_hyde': 'Additionally, we investigate the integration of Text-to-SQL with other natural language processing tasks, including question answering and information extraction, with the aim of developing more robust and versatile systems, as explored in [Reference].'},\n",
    " {'statement': 'For example, the AmbiQT benchmark addresses ambiguity in SQL by encompassing four types of ambiguity, including column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates.',\n",
    "  'related_sen_id': [17],\n",
    "  'statement_hyde': 'For instance, the AmbiQT benchmark tackles SQL ambiguity by incorporating four distinct types of ambiguity: column ambiguity, table ambiguity, join ambiguity, and precomputed aggregates, as described in [Reference].'},\n",
    " {'statement': 'Furthermore, the work on interactive Text-to-SQL generation proposes a new interaction mechanism for users to validate and refine generated queries through step-by-step explanations, which can be extended to support multi-turn SQL generation by incorporating the contextual information of previous queries into explanation generation and text-to-clause generation.',\n",
    "  'related_sen_id': [18],\n",
    "  'statement_hyde': 'Moreover, research on interactive Text-to-SQL generation introduces a novel interaction mechanism enabling users to validate and refine generated queries via step-by-step explanations, a method that can be extended to support multi-turn SQL generation by integrating the contextual information from prior queries into both explanation generation and text-to-clause generation processes, as detailed in [Reference].'},\n",
    " {'statement': \"Additionally, the exploration of chain-of-thought style prompting for Text-to-SQL aims to enhance LLMs' reasoning ability by systematically exploring CoT style prompting for text-to-SQL parsing, addressing complex, multistep reasoning required for the task.\",\n",
    "  'related_sen_id': [19],\n",
    "  'statement_hyde': \"Furthermore, the examination of chain-of-thought style prompting in the context of Text-to-SQL seeks to augment large language models' reasoning capabilities through a systematic exploration of CoT style prompting for text-to-SQL parsing, thereby addressing the intricate, multistep reasoning demands of the task, as discussed in [Reference].\"},\n",
    " {'statement': 'Additionally, we address the ethical considerations associated with Text2SQL technology, particularly in sensitive domains, and discuss strategies for mitigating bias and ensuring fairness.',\n",
    "  'related_sen_id': [20],\n",
    "  'statement_hyde': 'Moreover, we delve into the ethical considerations pertinent to Text2SQL technology, especially within sensitive domains, and articulate strategies for bias mitigation and the assurance of fairness, as examined in [Reference].'},\n",
    " {'statement': 'Finally, we identify promising future research directions for advancing Text2SQL technology and unlocking its full potential for empowering users to access and analyze data more effectively.',\n",
    "  'related_sen_id': [21],\n",
    "  'statement_hyde': 'In conclusion, we pinpoint promising avenues for future research aimed at advancing Text2SQL technology, thereby unlocking its comprehensive potential to empower users in accessing and analyzing data more effectively, as proposed in [Reference].'}]\n",
    "statement = statements[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from research_agent.core.query import Query\n",
    "query = Query()\n",
    "statement_papers = await query.query_by_content(statement[\"statement_hyde\"],top_k=20)\n",
    "[len(x[\"entity\"][\"chunk_text\"]) for x in statement_papers]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
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